Struct tensorflow::eager::raw_ops::SparseApplyAdagradDA

pub struct SparseApplyAdagradDA { /* private fields */ }

SparseApplyAdagradDA

See : https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseApplyAdagradDA

Implementations

impl SparseApplyAdagradDA

pub fn new() -> Self

Creates a new SparseApplyAdagradDA.

pub fn use_locking<ArgType: Into<bool>>(self, value: ArgType) -> Self

Sets the use_locking attribute.

pub fn target_device_name<ArgType: Into<String>>(self, value: ArgType) -> Self

Sets the `` attribute.

pub fn call<'a, T0: ToTensorHandle<'a>, T1: ToTensorHandle<'a>, T2: ToTensorHandle<'a>, T3: ToTensorHandle<'a>, T4: ToTensorHandle<'a>, T5: ToTensorHandle<'a>, T6: ToTensorHandle<'a>, T7: ToTensorHandle<'a>, T8: ToTensorHandle<'a>>( &self, ctx: &'a Context, var: &T0, gradient_accumulator: &T1, gradient_squared_accumulator: &T2, grad: &T3, indices: &T4, lr: &T5, l1: &T6, l2: &T7, global_step: &T8 ) -> Result<TensorHandle<'a>>

Execute sparse_apply_adagrad_da.

Trait Implementations

impl Clone for SparseApplyAdagradDA

fn clone(&self) -> SparseApplyAdagradDA

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SparseApplyAdagradDA

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for SparseApplyAdagradDA

fn default() -> Self

Returns the “default value” for a type.