minimize(self, loss, global_step=None, var_list=None, gate_gradients=1, aggregation_method=None, colocate_gradients_with_ops=False, name=None, grad_loss=None)
Add operations to minimize `loss` by updating `var_list`.
This method simply combines calls `compute_gradients()` and
`apply_gradients()`. If you want to process the gradient before applying
them call `compute_gradients()` and `apply_gradients()` explicitly instead
of using this function.
Args:
loss: A `Tensor` containing the value to minimize.
global_step: Optional `Variable` to increment by one after the
variables have been updated.
var_list: Optional list or tuple of `Variable` objects to update to
minimize `loss`. Defaults to the list of variables collected in
the graph under the key `GraphKeys.TRAINABLE_VARIABLES`.
gate_gradients: How to gate the computation of gradients. Can be
`GATE_NONE`, `GATE_OP`, or `GATE_GRAPH`.
aggregation_method: Specifies the method used to combine gradient terms.
Valid values are defined in the class `AggregationMethod`.
colocate_gradients_with_ops: If True, try colocating gradients with
the corresponding op.
name: Optional name for the returned operation.
grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`.
Returns:
An Operation that updates the variables in `var_list`. If `global_step`
was not `None`, that operation also increments `global_step`.
