Update relevant entries in '*var' according to the Ftrl-proximal scheme.
tf.raw_ops.SparseApplyFtrl( var, accum, linear, grad, indices, lr, l1, l2, lr_power, use_locking=False, multiply_linear_by_lr=False, name=None )
That is for rows we have grad for, we update var, accum and linear as follows:
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. |
indices | A Tensor . Must be one of the following types: int32 , int64 . A vector of indices into the first dimension of var and accum. |
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/SparseApplyFtrl