def _resource_apply_dense(self, grad, var, 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))
acc = self.get_slot(var, 'accumulator')
return training_ops.resource_apply_adagrad_v2(
var.handle,
acc.handle,
coefficients['lr_t'],
coefficients['epsilon'],
grad,
use_locking=self._use_locking)
def _resource_apply_dense(self, grad, var, constraint, 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))
acc = self.get_slot(var, 'accumulator')
var_update = training_ops.resource_apply_adagrad_v2(
var.handle,
acc.handle,
coefficients['lr_t'],
coefficients['epsilon'],
grad,
use_locking=self._use_locking)
project_var, was_projected = constraint.euclidean_project(var)
return state_ops.assign(var, project_var)
def _resource_apply_dense(self, grad, var, 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))
acc = self.get_slot(var, 'accumulator')
if compat.forward_compatible(2019, 8, 20):
return training_ops.resource_apply_adagrad_v2(
var.handle,
acc.handle,
coefficients['lr_t'],
coefficients['epsilon'],
grad,
use_locking=self._use_locking)
acc_t = state_ops.assign_add(acc,
math_ops.square(grad),
use_locking=self._use_locking)
var_update = state_ops.assign_sub(
var, coefficients['lr_t'] * grad /
(math_ops.sqrt(acc_t) + coefficients['epsilon']))
return var_update