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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:

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

Nested Classes

 class SparseApplyAdagradV2.Options Optional attributes for SparseApplyAdagradV2

Public Methods

public Output<T> asOutput()

Returns the symbolic handle of a 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 SparseApplyAdagradV2<T> create(Scope scope, Operand<T> var, Operand<T> accum, Operand<T> lr, Operand<T> epsilon, Operand<T> grad, Operand<U> indices, Options... options)

Parameters
scope current scope Should be from a Variable(). Should be from a Variable(). Learning rate. Must be a scalar. Constant factor. Must be a scalar. The gradient. A vector of indices into the first dimension of var and accum. carries optional attributes values
Returns
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.