class Kuku_01_12(KukuBaseModel): def __init__(self, input_dim=784, num_classes=10, dropout=0.3): super().__init__(input_dim, num_classes) self.net = nn.Sequential( nn.Linear(input_dim, 512), nn.ReLU(), nn.Dropout(dropout), nn.Linear(512, 512), nn.ReLU(), nn.Dropout(dropout), nn.Linear(512, 256), nn.ReLU(), nn.Linear(256, 128), nn.ReLU(), nn.Linear(128, num_classes) )
Now the magic: train all 15 variants with identical conditions. ptl models kuku model set 01 15
If you are referring to or similar units, here is a general guide for the 01–15 configuration/setup based on standard operating procedures. 🚦 PTL Type-1 Trailer Set Setup Guide class Kuku_01_12(KukuBaseModel): def __init__(self
trainer = pl.Trainer( max_epochs=20, accelerator="auto", callbacks=[checkpoint_callback, EarlyStopping(monitor="val_loss", patience=3)], log_every_n_steps=10 ) num_classes) self.net = nn.Sequential( nn.Linear(input_dim
Each set will live in its own branch or subfolder, making it trivial to compare across sets.
Sophisticated three-point lighting setups that highlight form and texture.