[incubator-mxnet] branch master updated: remove warning in tutorial: (#15135)

[ptrendx]

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ptrendx pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/master by this push:
     new 9125f6a  remove warning in tutorial: (#15135)
9125f6a is described below

commit 9125f6ae8cf0fb11338f18cd27223b5ce367afbc
Author: Lai Wei <roywei...@gmail.com>
AuthorDate: Mon Jun 3 08:02:17 2019 -0700

    remove warning in tutorial: (#15135)
---
 docs/tutorials/amp/amp_tutorial.md | 13 ++-----------
 1 file changed, 2 insertions(+), 11 deletions(-)

diff --git a/docs/tutorials/amp/amp_tutorial.md 
b/docs/tutorials/amp/amp_tutorial.md
index 02bf82a..be18929 100644
--- a/docs/tutorials/amp/amp_tutorial.md
+++ b/docs/tutorials/amp/amp_tutorial.md
@@ -92,10 +92,9 @@ train_data = SyntheticDataLoader(data_shape, batch_size)
 def get_network():
     # SSD with RN50 backbone
     net_name = 'ssd_512_resnet50_v1_coco'
-    net = get_model(net_name, pretrained_base=True, 
norm_layer=gluon.nn.BatchNorm)
-    async_net = net
     with warnings.catch_warnings(record=True) as w:
-        warnings.simplefilter("always")
+        warnings.simplefilter("ignore")
+        net = get_model(net_name, pretrained_base=True, 
norm_layer=gluon.nn.BatchNorm)
         net.initialize()
         net.collect_params().reset_ctx(ctx)
 
@@ -112,9 +111,6 @@ net = get_network()
 net.hybridize(static_alloc=True, static_shape=True)
 ```
 
-    /mxnet/code/python/mxnet/gluon/block.py:1138: UserWarning: Cannot decide 
type for the following arguments. Consider providing them as input:
-       data: None
-      input_sym_arg_type = in_param.infer_type()[0]
 
 
 Next, we need to create a Gluon Trainer.
@@ -192,11 +188,6 @@ net = get_network()
 net.hybridize(static_alloc=True, static_shape=True)
 ```
 
-    /mxnet/code/python/mxnet/gluon/block.py:1138: UserWarning: Cannot decide 
type for the following arguments. Consider providing them as input:
-       data: None
-      input_sym_arg_type = in_param.infer_type()[0]
-
-
 For some models that may be enough to start training in mixed precision, but 
the full FP16 recipe recommends using dynamic loss scaling to guard against 
over- and underflows of FP16 values. Therefore, as a next step, we create a 
trainer and initialize it with support for AMP's dynamic loss scaling. 
Currently, support for dynamic loss scaling is limited to trainers created with 
`update_on_kvstore=False` option, and so we add it to our trainer 
initialization.