def update_op_asynchronous(self, optimizer, g, index):
if isinstance(g, ops.Tensor):
return optimizer._apply_dense(g, self._v, index)
else:
assert isinstance(g, ops.IndexedSlices), ("Gradient ", g, " is neither a "
"tensor nor IndexedSlices.")
# pylint: disable=protected-access
return optimizer._apply_sparse_duplicate_indices(g, self._v, index)
def update_op_asynchronous(self, optimizer, g, index):
if isinstance(g, ops.Tensor):
return optimizer._apply_dense(g, self._v, index)
else:
assert isinstance(g,
ops.IndexedSlices), ("Gradient ", g,
" is neither a "
"tensor nor IndexedSlices.")
# pylint: disable=protected-access
return optimizer._apply_sparse_duplicate_indices(g, self._v, index)
def update_op(self, optimizer, loss, grad, global_step):
if isinstance(grad, ops.Tensor):
update_op = optimizer._apply_dense(loss, grad, self.var, global_step)
if self.var.constraint is not None:
with ops.control_dependencies([update_op]):
return self.var.assign(self.var.constraint(self.var))
else:
return update_op
else:
assert isinstance(grad, ops.IndexedSlices), ("Gradient ", grad, " is neither a tensor nor IndexedSlices.")
if self.var.constraint is not None:
raise RuntimeError("Cannot use a constraint function on a sparse variable.")
return optimizer._apply_sparse_duplicate_indices(grad, self.var)
def update_op(self, optimizer, g):
if isinstance(g, ops.Tensor):
update_op = optimizer._apply_dense(g, self._v) # pylint: disable=protected-access
if self._v.constraint is not None:
with ops.control_dependencies([update_op]):
return self._v.assign(self._v.constraint(self._v))
else:
return update_op
else:
assert isinstance(g,
ops.IndexedSlices), ("Gradient ", g,
" is neither a "
"tensor nor IndexedSlices.")
if self._v.constraint is not None:
raise RuntimeError(
"Cannot use a constraint function on a sparse variable.")
# pylint: disable=protected-access
return optimizer._apply_sparse_duplicate_indices(g, self._v)