Method update

tensorflow/python/training/optimizer.py:784–810

Apply gradients to a replica variable.

(v, g)

        self._create_slots(var_list)

      def update(v, g):
        """Apply gradients to a replica variable."""
        assert v is not None

        try:
          # Convert the grad to Tensor or IndexedSlices if necessary.
          g = ops.convert_to_tensor_or_indexed_slices(g)
        except TypeError:
          raise TypeError("Gradient must be convertible to a Tensor"
                          " or IndexedSlices, or None: %s" % g)
        if not isinstance(g, (ops.Tensor, ops.IndexedSlices)):
          raise TypeError(
              "Gradient must be a Tensor, IndexedSlices, or None: %s" % g)
        p = _get_processor(v)

        if context.executing_eagerly() or (
            resource_variable_ops.is_resource_variable(v) and
            not v._in_graph_mode):  # pylint: disable=protected-access
          scope_name = v.name.split(":")[0]
        else:
          scope_name = v.op.name

        # device_policy is set because non-mirrored tensors will be read in
        # `update_op`. `_resource_apply_dense`, `lr_t`, `beta1_t` and `beta2_t`
        # is an example.
        with ops.name_scope("update_" + scope_name):
          return p.update_op(self, g)

      with ops.name_scope(name, self._name) as sname:
        self._prepare()