Update '*var' according to the Ftrl-proximal scheme.
grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad * grad linear += grad_with_shrinkage - (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
Nested Classes
class | ApplyFtrl.Options | Optional attributes for ApplyFtrl
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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.
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static <T extends TType> ApplyFtrl<T> | |
static ApplyFtrl.Options |
multiplyLinearByLr(Boolean multiplyLinearByLr)
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Output<T> |
out()
Same as "var".
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static ApplyFtrl.Options |
useLocking(Boolean useLocking)
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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 ApplyFtrl<T> create (Scope scope, Operand<T> var, Operand<T> accum, Operand<T> linear, Operand<T> grad, Operand<T> lr, Operand<T> l1, Operand<T> l2, Operand<T> l2Shrinkage, Operand<T> lrPower, Options... options)
Factory method to create a class wrapping a new ApplyFtrl operation.
Parameters
scope | current scope |
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var | Should be from a Variable(). |
accum | Should be from a Variable(). |
linear | Should be from a Variable(). |
grad | The gradient. |
lr | Scaling factor. Must be a scalar. |
l1 | L1 regularization. Must be a scalar. |
l2 | L2 shrinkage regularization. Must be a scalar. |
lrPower | Scaling factor. Must be a scalar. |
options | carries optional attributes values |
Returns
- a new instance of ApplyFtrl
public static ApplyFtrl.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. |
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