def build_model(self) -> nn.Module:
model = nn.Sequential(
nn.Conv2d(NUM_CHANNELS, IMAGE_SIZE, kernel_size=(3, 3)),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=(3, 3)),
nn.ReLU(),
nn.MaxPool2d((2, 2)),
nn.Dropout2d(pedl.get_hyperparameter("layer1_dropout")),
nn.Conv2d(32, 64, (3, 3), padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, (3, 3)),
nn.ReLU(),
nn.MaxPool2d((2, 2)),
nn.Dropout2d(pedl.get_hyperparameter("layer2_dropout")),
Flatten(),
nn.Linear(2304, 512),
nn.ReLU(),
nn.Dropout2d(pedl.get_hyperparameter("layer3_dropout")),
nn.Linear(512, NUM_CLASSES),
nn.Softmax(dim=0),
)
# If loading backbone weights, do not call reset_parameters() or
# call before loading the backbone weights.
reset_parameters(model)
return model
def build_model(self) -> nn.Module:
model = MultiNet()
# If loading backbone weights, do not call reset_parameters() or
# call before loading the backbone weights.
reset_parameters(model)
return model
def build_model(self) -> nn.Module:
model = nn.Sequential(
nn.Conv2d(1, pedl.get_hyperparameter("n_filters1"), kernel_size=5),
nn.MaxPool2d(2),
nn.ReLU(),
nn.Conv2d(
pedl.get_hyperparameter("n_filters1"),
pedl.get_hyperparameter("n_filters2"),
kernel_size=5,
),
nn.MaxPool2d(2),
nn.ReLU(),
Flatten(),
nn.Linear(16 * pedl.get_hyperparameter("n_filters2"), 50),
nn.ReLU(),
nn.Dropout2d(pedl.get_hyperparameter("dropout")),
nn.Linear(50, 10),
nn.LogSoftmax(),
)
# If loading backbone weights, do not call reset_parameters() or
# call before loading the backbone weights.
reset_parameters(model)
return model