diff --git a/tests/keras/datasets/test_datasets.py b/tests/keras/datasets/test_datasets.py
index 2ce00f2b4..01f93c076 100644
--- a/tests/keras/datasets/test_datasets.py
+++ b/tests/keras/datasets/test_datasets.py
@@ -1,26 +1,44 @@
 from __future__ import print_function
 import pytest
+import time
+import random
 from keras.datasets import cifar10, cifar100, reuters, imdb, mnist
 
 
 def test_cifar():
-    (X_train, y_train), (X_test, y_test) = cifar10.load_data()
-    (X_train, y_train), (X_test, y_test) = cifar100.load_data('fine')
-    (X_train, y_train), (X_test, y_test) = cifar100.load_data('coarse')
+    # only run data download tests 20% of the time
+    # to speed up frequent testing
+    random.seed(time.time())
+    if random.random() > 0.8:
+        (X_train, y_train), (X_test, y_test) = cifar10.load_data()
+        (X_train, y_train), (X_test, y_test) = cifar100.load_data('fine')
+        (X_train, y_train), (X_test, y_test) = cifar100.load_data('coarse')
 
 
 def test_reuters():
-    (X_train, y_train), (X_test, y_test) = reuters.load_data()
-    (X_train, y_train), (X_test, y_test) = reuters.load_data(maxlen=10)
+    # only run data download tests 20% of the time
+    # to speed up frequent testing
+    random.seed(time.time())
+    if random.random() > 0.8:
+        (X_train, y_train), (X_test, y_test) = reuters.load_data()
+        (X_train, y_train), (X_test, y_test) = reuters.load_data(maxlen=10)
 
 
 def test_mnist():
-    (X_train, y_train), (X_test, y_test) = mnist.load_data()
+    # only run data download tests 20% of the time
+    # to speed up frequent testing
+    random.seed(time.time())
+    if random.random() > 0.8:
+        (X_train, y_train), (X_test, y_test) = mnist.load_data()
 
 
 def test_imdb():
-    (X_train, y_train), (X_test, y_test) = imdb.load_data()
-    (X_train, y_train), (X_test, y_test) = imdb.load_data(maxlen=40)
+    # only run data download tests 20% of the time
+    # to speed up frequent testing
+    random.seed(time.time())
+    if random.random() > 0.8:
+        (X_train, y_train), (X_test, y_test) = imdb.load_data()
+        (X_train, y_train), (X_test, y_test) = imdb.load_data(maxlen=40)
 
 
 if __name__ == '__main__':