From 798083264a5dd63e68f7c6a0e5704ed78b7e5436 Mon Sep 17 00:00:00 2001
From: Lauren Yu <6631887+laurenyu@users.noreply.github.com>
Date: Wed, 27 Jun 2018 15:22:13 -0700
Subject: [PATCH] Lower learning rate for TF cifar integ test

We've been seeing a lot of errors that say failure due to NaN loss.
One suggestion from https://stackoverflow.com/questions/40050397/deep-learning-nan-loss-reasons
is to use a lower number for learning rate.
---
 tests/data/cifar_10/source/resnet_cifar_10.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/data/cifar_10/source/resnet_cifar_10.py b/tests/data/cifar_10/source/resnet_cifar_10.py
index 47d416ebcf..39101f1fb6 100644
--- a/tests/data/cifar_10/source/resnet_cifar_10.py
+++ b/tests/data/cifar_10/source/resnet_cifar_10.py
@@ -33,8 +33,8 @@
 BATCH_SIZE = 1
 
 # Scale the learning rate linearly with the batch size. When the batch size is
-# 128, the learning rate should be 0.1.
-_INITIAL_LEARNING_RATE = 0.1 * BATCH_SIZE / 128
+# 128, the learning rate should be 0.05.
+_INITIAL_LEARNING_RATE = 0.05 * BATCH_SIZE / 128
 _MOMENTUM = 0.9
 
 # We use a weight decay of 0.0002, which performs better than the 0.0001 that