official/mnist: Updates with the release of TensorFlow 1.8.

official/mnist/mnist_eager.py

@@ -63,7 +63,7 @@ def train(model, optimizer, dataset, step_counter, log_interval=None):
       # Record the operations used to compute the loss given the input,
       # so that the gradient of the loss with respect to the variables
       # can be computed.
-      with tfe.GradientTape() as tape:
+      with tf.GradientTape() as tape:
         logits = model(images, training=True)
         loss_value = loss(logits, labels)
         tf.contrib.summary.scalar('loss', loss_value)
@@ -99,11 +99,11 @@ def main(argv):
   parser = MNISTEagerArgParser()
   flags = parser.parse_args(args=argv[1:])
 
-  tfe.enable_eager_execution()
+  tf.enable_eager_execution()
 
   # Automatically determine device and data_format
   (device, data_format) = ('/gpu:0', 'channels_first')
-  if flags.no_gpu or tfe.num_gpus() <= 0:
+  if flags.no_gpu or not tf.test.is_gpu_available():
     (device, data_format) = ('/cpu:0', 'channels_last')
   # If data_format is defined in FLAGS, overwrite automatically set value.
   if flags.data_format is not None: