tf.raw_ops.ResourceSparseApplyProximalGradientDescent
Sparse update '*var' as FOBOS algorithm with fixed learning rate.
tf.raw_ops.ResourceSparseApplyProximalGradientDescent(
var, alpha, l1, l2, grad, indices, use_locking=False, name=None
)
That is for rows we have grad for, we update var as follows: prox_v = var - alpha * grad var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
| Args | |
|---|---|
var |
A Tensor of type resource. Should be from a Variable(). |
alpha |
A 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. Scaling factor. Must be a scalar. |
l1 |
A Tensor. Must have the same type as alpha. L1 regularization. Must be a scalar. |
l2 |
A Tensor. Must have the same type as alpha. L2 regularization. Must be a scalar. |
grad |
A Tensor. Must have the same type as alpha. The gradient. |
indices |
A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum. |
use_locking |
An optional bool. Defaults to False. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. |
name |
A name for the operation (optional). |
| Returns | |
|---|---|
| The created Operation. |
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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/raw_ops/ResourceSparseApplyProximalGradientDescent