def _resource_apply_sparse(self, grad, var, indices):
acc = self.get_slot(var, "accumulator")
return training_ops.resource_sparse_apply_adagrad(
var.handle,
acc.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices, state):
acc = state.get_slot(var, "accumulator")
return training_ops.resource_sparse_apply_adagrad(
var.handle,
acc.handle,
state.get_hyper("learning_rate", var.dtype.base_dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices, state):
acc = state.get_slot(var, "accumulator")
return training_ops.resource_sparse_apply_adagrad(
var.handle,
acc.handle,
state.get_hyper("learning_rate", var.dtype.base_dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices):
acc = self.get_slot(var, "accumulator")
return training_ops.resource_sparse_apply_adagrad(
var.handle,
acc.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices):
acc = self.get_slot(var, "accumulator")
if isinstance(var, embedding_variable_ops.EmbeddingVariable):
global_step = training_util.get_or_create_global_step()
return gen_ev_ops.ev_sparse_apply_adagrad(
var.handle,
acc.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype),
grad,
indices,
global_step,
use_locking=self._use_locking)
else:
return training_ops.resource_sparse_apply_adagrad(
var.handle,
acc.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype),
grad,
indices,
use_locking=self._use_locking)
