diff --git a/docker/tune_test/Dockerfile b/docker/tune_test/Dockerfile
index f88b441b6..623a35ba1 100644
--- a/docker/tune_test/Dockerfile
+++ b/docker/tune_test/Dockerfile
@@ -6,7 +6,8 @@ FROM ray-project/base-deps
 # a test runner.
 RUN conda install -y numpy
 RUN pip install -U pip
-RUN pip install -U https://ray-wheels.s3-us-west-2.amazonaws.com/latest/ray-0.8.0.dev6-cp36-cp36m-manylinux1_x86_64.whl boto3
+RUN pip install -U https://ray-wheels.s3-us-west-2.amazonaws.com/latest/ray-0.8.0.dev6-cp36-cp36m-manylinux1_x86_64.whl || pip install -U https://ray-wheels.s3-us-west-2.amazonaws.com/latest/ray-0.8.0.dev5-cp36-cp36m-manylinux1_x86_64.whl
+RUN pip install -U boto3
 # We install this after the latest wheels -- this should not override the latest wheels.
 RUN apt-get install -y zlib1g-dev
 # The following is needed to support TensorFlow 1.14
diff --git a/python/ray/tune/integration/keras.py b/python/ray/tune/integration/keras.py
index 0131dac5d..0df8cf2d3 100644
--- a/python/ray/tune/integration/keras.py
+++ b/python/ray/tune/integration/keras.py
@@ -2,7 +2,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import keras
+from tensorflow import keras
 from ray.tune import track
 
 
@@ -32,7 +32,6 @@ class TuneReporterCallback(keras.callbacks.Callback):
         for metric in list(logs):
             if "loss" in metric and "neg_" not in metric:
                 logs["neg_" + metric] = -logs[metric]
-        print(logs)
         if "acc" in logs:
             self.reporter(keras_info=logs, mean_accuracy=logs["acc"])
         else:
@@ -45,4 +44,7 @@ class TuneReporterCallback(keras.callbacks.Callback):
         for metric in list(logs):
             if "loss" in metric and "neg_" not in metric:
                 logs["neg_" + metric] = -logs[metric]
-        self.reporter(keras_info=logs, mean_accuracy=logs["acc"])
+        if "acc" in logs:
+            self.reporter(keras_info=logs, mean_accuracy=logs["acc"])
+        else:
+            self.reporter(keras_info=logs, mean_accuracy=logs.get("accuracy"))