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))
acc = self.get_slot(var, 'accumulator')
return training_ops.resource_sparse_apply_adagrad_v2(
var.handle,
acc.handle,
coefficients['lr_t'],
coefficients['epsilon'],
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))
acc = self.get_slot(var, 'accumulator')
if compat.forward_compatible(2019, 8, 20):
return training_ops.resource_sparse_apply_adagrad_v2(
var.handle,
acc.handle,
coefficients['lr_t'],
coefficients['epsilon'],
grad,
indices,
use_locking=self._use_locking)
with ops.control_dependencies([
resource_variable_ops.resource_scatter_add(
acc.handle, indices, math_ops.square(grad))
]):
acc_t_slice = acc.sparse_read(indices)
var_update = resource_variable_ops.resource_scatter_add(
var.handle, indices, coefficients['neg_lr_t'] * grad /
(math_ops.sqrt(acc_t_slice) + coefficients['epsilon']))
return var_update
