tf_privacy.DPSequential

DP subclass of tf.keras.Sequential.

tf_privacy.DPSequential(
    l2_norm_clip, noise_multiplier, use_xla=True, *args, **kwargs
)

This can be used as a differentially private replacement for tf.keras.Sequential. This class implements DP-SGD using the standard Gaussian mechanism.

When instantiating this class, you need to supply several DP-related arguments followed by the standard arguments for Sequential.

Examples:

# Create Model instance.
model = DPSequential(l2_norm_clip=1.0, noise_multiplier=0.5, use_xla=True,
         <standard arguments>)

You should use your DPSequential instance with a standard instance of tf.keras.Optimizer as the optimizer, and a standard reduced loss. You do not need to use a differentially private optimizer.

# Use a standard (non-DP) optimizer.
optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)

# Use a standard reduced loss.
loss = tf.keras.losses.MeanSquaredError()

model.compile(optimizer=optimizer, loss=loss)
model.fit(train_data, train_labels, epochs=1, batch_size=32)

Args
l2_norm_clip	Clipping norm (max L2 norm of per microbatch gradients).
noise_multiplier	Ratio of the standard deviation to the clipping norm.
use_xla	If True, compiles train_step to XLA.
*args	These will be passed on to the base class __init__ method.
**kwargs	These will be passed on to the base class __init__ method.

Methods

add

add(
    layer
)

Adds a layer instance on top of the layer stack.

Args
layer	layer instance.

Raises
TypeError	If layer is not a layer instance.
ValueError	In case the layer argument does not know its input shape.
ValueError	In case the layer argument has multiple output tensors, or is already connected somewhere else (forbidden in Sequential models).

pop

pop()

Removes the last layer in the model.

Raises
TypeError	if there are no layers in the model.