Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
That is for rows we have grad for, we update var and accum as follows: $$accum += grad grad$$ $$prox_v = var$$ $$prox_v -= lr grad (1 / sqrt(accum))$$ $$var = sign(prox_v)/(1+lrl2) max{|prox_v|-lrl1,0}$$
Nested Classes
class | SparseApplyProximalAdagrad.Options | Optional attributes for SparseApplyProximalAdagrad
|
Constants
String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
Output<T> |
asOutput()
Returns the symbolic handle of the tensor.
|
static <T extends TType> SparseApplyProximalAdagrad<T> | |
Output<T> |
out()
Same as "var".
|
static SparseApplyProximalAdagrad.Options |
useLocking(Boolean useLocking)
|
Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Public Methods
public Output<T> asOutput ()
Returns the symbolic handle of the tensor.
Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.
public static SparseApplyProximalAdagrad<T> create (Scope scope, Operand<T> var, Operand<T> accum, Operand<T> lr, Operand<T> l1, Operand<T> l2, Operand<T> grad, Operand<? extends TNumber> indices, Options... options)
Factory method to create a class wrapping a new SparseApplyProximalAdagrad operation.
Parameters
scope | current scope |
---|---|
var | Should be from a Variable(). |
accum | Should be from a Variable(). |
lr | Learning rate. Must be a scalar. |
l1 | L1 regularization. Must be a scalar. |
l2 | L2 regularization. Must be a scalar. |
grad | The gradient. |
indices | A vector of indices into the first dimension of var and accum. |
options | carries optional attributes values |
Returns
- a new instance of SparseApplyProximalAdagrad
public static SparseApplyProximalAdagrad.Options useLocking (Boolean useLocking)
Parameters
useLocking | 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. |
---|