def __init__(self, context: det.TrialContext) -> None:
self.context = context
self.data_config = context.get_data_config()
self.criterion = CrossEntropyLabelSmooth(
context.get_hparam("num_classes"), # num classes
context.get_hparam("label_smoothing_rate"),
)
self.last_epoch_idx = -1
def __init__(self, context: det.TrialContext) -> None:
self.context = context
self.data_config = context.get_data_config()
self.num_classes = {
"train": context.get_hparam("num_classes_train"),
"val": context.get_hparam("num_classes_val"),
}
self.num_support = {
"train": context.get_hparam("num_support_train"),
"val": context.get_hparam("num_support_val"),
}
self.num_query = {
"train": context.get_hparam("num_query_train"),
"val": None, # Use all available examples for val at meta-test time
}
self.get_train_valid_splits()
def __init__(self, context: det.TrialContext) -> None:
super().__init__()
# Set hyperparameters that influence the model architecture.
self.n_filters1 = context.get_hparam("n_filters1")
self.n_filters2 = context.get_hparam("n_filters2")
self.dropout = context.get_hparam("dropout")
# Define the central model.
self.model = nn.Sequential(
nn.Conv2d(1, self.n_filters1, kernel_size=5),
nn.MaxPool2d(2),
nn.ReLU(),
nn.Conv2d(self.n_filters1, self.n_filters2, kernel_size=5),
nn.MaxPool2d(2),
nn.ReLU(),
Flatten(),
nn.Linear(16 * self.n_filters2, 50),
nn.ReLU(),
nn.Dropout2d(self.dropout),
) # type: nn.Sequential
# Predict digit labels from self.model.
self.digit = nn.Sequential(nn.Linear(50, 10), nn.Softmax(dim=0))
# Predict binary labels from self.model.
self.binary = nn.Sequential(nn.Linear(50, 1), nn.Sigmoid(), Squeeze())