T - data type for out() output@Operator(group="train") public final class SparseApplyProximalAdagrad<T> extends PrimitiveOp implements Operand<T>
That is for rows we have grad for, we update var and accum as follows: $$accum += grad grad$$ $$prox_v = var$$ $$prox_v -= lr grad (1 / sqrt(accum))$$ $$var = sign(prox_v)/(1+lrl2) max{|prox_v|-lrl1,0}$$
| Modifier and Type | Class and Description |
|---|---|
static class |
SparseApplyProximalAdagrad.Options
Optional attributes for
SparseApplyProximalAdagrad |
operation| Modifier and Type | Method and Description |
|---|---|
Output<T> |
asOutput()
Returns the symbolic handle of a tensor.
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static <T,U extends Number> |
create(Scope scope,
Operand<T> var,
Operand<T> accum,
Operand<T> lr,
Operand<T> l1,
Operand<T> l2,
Operand<T> grad,
Operand<U> indices,
SparseApplyProximalAdagrad.Options... options)
Factory method to create a class wrapping a new SparseApplyProximalAdagrad operation.
|
Output<T> |
out()
Same as "var".
|
static SparseApplyProximalAdagrad.Options |
useLocking(Boolean useLocking) |
equals, hashCode, op, toStringpublic static <T,U extends Number> SparseApplyProximalAdagrad<T> create(Scope scope, Operand<T> var, Operand<T> accum, Operand<T> lr, Operand<T> l1, Operand<T> l2, Operand<T> grad, Operand<U> indices, SparseApplyProximalAdagrad.Options... options)
scope - current scopevar - 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 valuespublic static SparseApplyProximalAdagrad.Options useLocking(Boolean useLocking)
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.public Output<T> asOutput()
OperandInputs 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.
asOutput in interface Operand<T>OperationBuilder.addInput(Output)Copyright © 2022. All rights reserved.