tf.keras.regularizers.l1_l2
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Create a regularizer that applies both L1 and L2 penalties.
tf.keras.regularizers.l1_l2(
l1=0.01, l2=0.01
)
The L1 regularization penalty is computed as:
loss = l1 * reduce_sum(abs(x))
The L2 regularization penalty is computed as:
loss = l2 * reduce_sum(square(x))
Arguments |
l1
|
Float; L1 regularization factor.
|
l2
|
Float; L2 regularization factor.
|
Returns |
An L1L2 Regularizer with the given regularization factors.
|
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Last updated 2021-02-18 UTC.
[null,null,["Last updated 2021-02-18 UTC."],[],[],null,["# tf.keras.regularizers.l1_l2\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/keras/regularizers/l1_l2) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.4.0/tensorflow/python/keras/regularizers.py#L328-L345) |\n\nCreate a regularizer that applies both L1 and L2 penalties.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.regularizers.l1_l2`](https://www.tensorflow.org/api_docs/python/tf/keras/regularizers/l1_l2)\n\n\u003cbr /\u003e\n\n tf.keras.regularizers.l1_l2(\n l1=0.01, l2=0.01\n )\n\nThe L1 regularization penalty is computed as:\n`loss = l1 * reduce_sum(abs(x))`\n\nThe L2 regularization penalty is computed as:\n`loss = l2 * reduce_sum(square(x))`\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|------|----------------------------------|\n| `l1` | Float; L1 regularization factor. |\n| `l2` | Float; L2 regularization factor. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| An L1L2 Regularizer with the given regularization factors. ||\n\n\u003cbr /\u003e"]]