tf.raw_ops.ApplyFtrl
Update '*var' according to the Ftrl-proximal scheme.
tf.raw_ops.ApplyFtrl(
var, accum, linear, grad, lr, l1, l2, lr_power, use_locking=False,
multiply_linear_by_lr=False, name=None
)
accum_new = accum + grad * grad linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new
| Args | |
|---|---|
var |
A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable(). |
accum |
A mutable Tensor. Must have the same type as var. Should be from a Variable(). |
linear |
A mutable Tensor. Must have the same type as var. Should be from a Variable(). |
grad |
A Tensor. Must have the same type as var. The gradient. |
lr |
A Tensor. Must have the same type as var. Scaling factor. Must be a scalar. |
l1 |
A Tensor. Must have the same type as var. L1 regularization. Must be a scalar. |
l2 |
A Tensor. Must have the same type as var. L2 regularization. Must be a scalar. |
lr_power |
A Tensor. Must have the same type as var. Scaling factor. Must be a scalar. |
use_locking |
An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. |
multiply_linear_by_lr |
An optional bool. Defaults to False. |
name |
A name for the operation (optional). |
| Returns | |
|---|---|
A mutable Tensor. Has the same type as var. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/raw_ops/ApplyFtrl