# Freezing Modules

Modules have a .frozen property that specifies whether the module is frozen or not, Modules such as Dropout and BatchNorm which will behave differently based on its value. To switch between modes, use the .freeze() and .unfreeze() methods, they return a new Module whose frozen state and the state of all of its submodules (recursively) are set to the desired value.

class ConvBlock(tx.Module):
...

model = tx.Sequential(
ConvBlock(3, 32),
ConvBlock(32, 64),
ConvBlock(64, 128),
...
)

# train model
...

# freeze some layers
for layer in model.layers[:-1]:
layer.freeze(inplace=True)

# fine-tune the model
...

In this example we can leveraged the fact that Sequential has its submodules in .layers to freeze all but the last layers.

Freezing modules is useful for tasks such as Transfer Learning where you want to keep most of the weights in a model unchange and train only a few of them on a new dataset. If you have a backbone you can just freeze the entire model.

backbone = get_pretrained_model()
backbone = backbone.freeze()

model = tx.Sequential(
backbone,
tx.Linear(backbone.output_features, 10)
).init(42)

...
# Initialize optimizer with only the trainable set of parameters
optimizer = optimizer.init(model.trainable_parameters())
...

@jax.jit
def train_step(model, x, y, optimizer):
# only differentiate w.r.t. parameters whose module is not frozen
params = model.trainable_parameters()
(loss, model), grads = loss_fn(params, model, x, y)

...