message: Update control_flow_ops.py
  * Update control_flow_ops.py
  Updated documentation of while_loop() function
  * Update control_flow_ops.py
diff: @@ -2670,8 +2670,8 @@ def while_loop(cond, body, loop_vars, shape_invariants=None,
  Note that `while_loop` calls `cond` and `body` *exactly once* (inside the
  call to `while_loop`, and not at all during `Session.run()`). `while_loop`
  stitches together the graph fragments created during the `cond` and `body`
  - calls with some additional graph nodes to make something the repeats
  - `body` until `cond` returns false.
  + calls with some additional graph nodes to create the graph flow that
  + repeats `body` until `cond` returns false.
  For correctness, `tf.while_loop()` strictly enforces shape invariants for
  the loop variables. A shape invariant is a (possibly partial) shape that
  @@ -2707,9 +2707,9 @@ def while_loop(cond, body, loop_vars, shape_invariants=None,
  memory consumption and execution order. For correct programs, `while_loop`
  should return the same result for any parallel_iterations > 0.
  - For training, TensorFlow remembers the tensors that are produced in the
  - forward inference but needed in back propagation. These tensors can be a
  - main source of memory consumption and often cause OOM problems when training
  + For training, TensorFlow stores the tensors that are produced in the
  + forward inference and are needed in back propagation. These tensors are a
  + main source of memory consumption and often cause OOM errors when training
  on GPUs. When the flag swap_memory is true, we swap out these tensors from
  GPU to CPU. This for example allows us to train RNN models with very long
  sequences and large batches.
  