def _apply_sparse(self, grad, var):
rms = self.get_slot(var, "rms")
mom = self.get_slot(var, "momentum")
if self._centered:
mg = self.get_slot(var, "mg")
return training_ops.sparse_apply_centered_rms_prop(
var,
mg,
rms,
mom,
math_ops.cast(self._learning_rate_tensor,
var.dtype.base_dtype),
math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
grad.values,
grad.indices,
use_locking=self._use_locking)
else:
return training_ops.sparse_apply_rms_prop(
var,
rms,
mom,
math_ops.cast(self._learning_rate_tensor,
var.dtype.base_dtype),
math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
grad.values,
grad.indices,
use_locking=self._use_locking)
def _apply_sparse(self, grad, var):
rms = self.get_slot(var, "rms")
mom = self.get_slot(var, "momentum")
if self._centered:
mg = self.get_slot(var, "mg")
return training_ops.sparse_apply_centered_rms_prop(
var,
mg,
rms,
mom,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
grad.values,
grad.indices,
use_locking=self._use_locking)
else:
return training_ops.sparse_apply_rms_prop(
var,
rms,
mom,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
grad.values,
grad.indices,
use_locking=self._use_locking)
def _apply_sparse(self, grad, var, state):
rms = state.get_slot(var, "rms")
mom = state.get_slot(var, "momentum")
if self._centered:
mg = state.get_slot(var, "mg")
return training_ops.sparse_apply_centered_rms_prop(
var,
mg,
rms,
mom,
state.get_hyper("learning_rate", var.dtype.base_dtype),
state.get_hyper("rho", var.dtype.base_dtype),
state.get_hyper("momentum", var.dtype.base_dtype),
0,
grad.values,
grad.indices,
use_locking=self._use_locking)
else:
return training_ops.sparse_apply_rms_prop(
var,
rms,
mom,
state.get_hyper("learning_rate", var.dtype.base_dtype),
state.get_hyper("rho", var.dtype.base_dtype),
state.get_hyper("momentum", var.dtype.base_dtype),
0,
grad.values,
grad.indices,
use_locking=self._use_locking)
def _apply_sparse(self, grad, var, state):
rms = state.get_slot(var, "rms")
mom = state.get_slot(var, "momentum")
if self._centered:
mg = state.get_slot(var, "mg")
return training_ops.sparse_apply_centered_rms_prop(
var,
mg,
rms,
mom,
state.get_hyper("learning_rate", var.dtype.base_dtype),
state.get_hyper("rho", var.dtype.base_dtype),
state.get_hyper("momentum", var.dtype.base_dtype),
0,
grad.values,
grad.indices,
use_locking=self._use_locking)
else:
return training_ops.sparse_apply_rms_prop(
var,
rms,
mom,
state.get_hyper("learning_rate", var.dtype.base_dtype),
state.get_hyper("rho", var.dtype.base_dtype),
state.get_hyper("momentum", var.dtype.base_dtype),
0,
grad.values,
grad.indices,
use_locking=self._use_locking)
