def _resource_apply_sparse(self, grad, var, indices, state):
accum = state.get_slot(var, "accum")
accum_update = state.get_slot(var, "accum_update")
return training_ops.resource_sparse_apply_adadelta(
var.handle,
accum.handle,
accum_update.handle,
state.get_hyper("learning_rate", var.dtype.base_dtype),
state.get_hyper("rho", var.dtype.base_dtype),
state.get_hyper("epsilon", var.dtype.base_dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices):
accum = self.get_slot(var, "accum")
accum_update = self.get_slot(var, "accum_update")
return training_ops.resource_sparse_apply_adadelta(
var.handle,
accum.handle,
accum_update.handle,
math_ops.cast(self._lr_t, grad.dtype),
math_ops.cast(self._rho_t, grad.dtype),
math_ops.cast(self._epsilon_t, grad.dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices):
accum_grad = self.get_slot(var, 'accum_grad')
accum_var = self.get_slot(var, 'accum_var')
return training_ops.resource_sparse_apply_adadelta(
var.handle,
accum_grad.handle,
accum_var.handle,
math_ops.cast(self._get_hyper('learning_rate'), grad.dtype.base_dtype),
math_ops.cast(self._get_hyper('rho'), grad.dtype.base_dtype),
math_ops.cast(self._get_hyper('epsilon'), grad.dtype.base_dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices, state):
accum = state.get_slot(var, "accum")
accum_update = state.get_slot(var, "accum_update")
return training_ops.resource_sparse_apply_adadelta(
var.handle,
accum.handle,
accum_update.handle,
state.get_hyper("learning_rate", var.dtype.base_dtype),
state.get_hyper("rho", var.dtype.base_dtype),
state.get_hyper("epsilon", var.dtype.base_dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices):
accum = self.get_slot(var, "accum")
accum_update = self.get_slot(var, "accum_update")
return training_ops.resource_sparse_apply_adadelta(
var.handle,
accum.handle,
accum_update.handle,
math_ops.cast(self._lr_t, grad.dtype),
math_ops.cast(self._rho_t, grad.dtype),
math_ops.cast(self._epsilon_t, grad.dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices):
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
accum_grad = self.get_slot(var, 'accum_grad')
accum_var = self.get_slot(var, 'accum_var')
return training_ops.resource_sparse_apply_adadelta(
var.handle,
accum_grad.handle,
accum_var.handle,
lr_t,
self._get_hyper('rho', var_dtype),
self._get_hyper('epsilon', var_dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices):
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
accum_grad = self.get_slot(var, 'accum_grad')
accum_var = self.get_slot(var, 'accum_var')
return training_ops.resource_sparse_apply_adadelta(
var.handle,
accum_grad.handle,
accum_var.handle,
lr_t,
self._get_hyper('rho', var_dtype),
self._get_hyper('epsilon', var_dtype),
grad,
indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
accum_grad = self.get_slot(var, 'accum_grad')
accum_var = self.get_slot(var, 'accum_var')
return training_ops.resource_sparse_apply_adadelta(
var.handle,
accum_grad.handle,
accum_var.handle,
coefficients['lr_t'],
coefficients['rho'],
coefficients['epsilon'],
grad,
indices,
use_locking=self._use_locking)
