tf.raw_ops.ResourceSparseApplyFtrl

Update relevant entries in '*var' according to the Ftrl-proximal scheme.

tf.raw_ops.ResourceSparseApplyFtrl(
    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: accum_new = accum + grad * grad linear += grad - (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

var A Tensor of type resource. Should be from a Variable(). accum A Tensor of type resource. Should be from a Variable(). linear A Tensor of type resource. Should be from a Variable(). grad A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, qint16, quint16, uint16, complex128, half, uint32, uint64. 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 grad. Scaling factor. Must be a scalar. l1 A Tensor. Must have the same type as grad. L1 regularization. Must be a scalar. l2 A Tensor. Must have the same type as grad. L2 regularization. Must be a scalar. lr_power A Tensor. Must have the same type as grad. 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).

The created Operation.