tf.raw_ops.SparseApplyAdagrad

Stay organized with collections Save and categorize content based on your preferences.

Update relevant entries in 'var' and 'accum' according to the adagrad scheme.

That is for rows we have grad for, we update var and accum as follows:

$$accum += grad * grad$$
$$var -= lr * grad * (1 / sqrt(accum))$$

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().
lr A Tensor. Must have the same type as var. Learning rate. Must be a scalar.
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.
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.
update_slots An optional bool. Defaults to True.
name A name for the operation (optional).

A mutable Tensor. Has the same type as var.