tf.keras.regularizers.Regularizer

Regularizer base class.

Regularizers allow you to apply penalties on layer parameters or layer activity during optimization. These penalties are summed into the loss function that the network optimizes.

Regularization penalties are applied on a per-layer basis. The exact API will depend on the layer, but many layers (e.g. Dense, Conv1D, Conv2D and Conv3D) have a unified API.

These layers expose 3 keyword arguments:

  • kernel_regularizer: Regularizer to apply a penalty on the layer's kernel
  • bias_regularizer: Regularizer to apply a penalty on the layer's bias
  • activity_regularizer: Regularizer to apply a penalty on the layer's output

All layers (including custom layers) expose activity_regularizer as a settable property, whether or not it is in the constructor arguments.

The value returned by the activity_regularizer is divided by the input batch size so that the relative weighting between the weight regularizers and the activity regularizers does not change with the batch size.

You can access a layer's regularization penalties by calling layer.losses after calling the layer on inputs.

Example

layer = tf.keras.layers.Dense(
    5, input_dim=5,
    kernel_initializer='ones',
    kernel_regular