[official/mnist]: Avoid some now unnecessary 'tfe' symbols.

official/mnist/mnist_eager.py

@@ -33,7 +33,6 @@ import time
 from absl import app as absl_app
 from absl import flags
 import tensorflow as tf
-import tensorflow.contrib.eager as tfe
 # pylint: enable=g-bad-import-order
 
 from official.mnist import dataset as mnist_dataset
@@ -42,6 +41,8 @@ from official.utils.flags import core as flags_core
 from official.utils.misc import model_helpers
 
 
+tfe = tf.contrib.eager
+
 def loss(logits, labels):
   return tf.reduce_mean(
       tf.nn.sparse_softmax_cross_entropy_with_logits(
@@ -60,7 +61,7 @@ def train(model, optimizer, dataset, step_counter, log_interval=None):
   """Trains model on `dataset` using `optimizer`."""
 
   start = time.time()
-  for (batch, (images, labels)) in enumerate(tfe.Iterator(dataset)):
+  for (batch, (images, labels)) in enumerate(dataset):
     with tf.contrib.summary.record_summaries_every_n_global_steps(
         10, global_step=step_counter):
       # Record the operations used to compute the loss given the input,
@@ -85,7 +86,7 @@ def test(model, dataset):
   avg_loss = tfe.metrics.Mean('loss')
   accuracy = tfe.metrics.Accuracy('accuracy')
 
-  for (images, labels) in tfe.Iterator(dataset):
+  for (images, labels) in dataset:
     logits = model(images, training=False)
     avg_loss(loss(logits, labels))
     accuracy(
@@ -145,7 +146,7 @@ def run_mnist_eager(flags_obj):
   # Create and restore checkpoint (if one exists on the path)
   checkpoint_prefix = os.path.join(flags_obj.model_dir, 'ckpt')
   step_counter = tf.train.get_or_create_global_step()
-  checkpoint = tfe.Checkpoint(
+  checkpoint = tf.train.Checkpoint(
       model=model, optimizer=optimizer, step_counter=step_counter)
   # Restore variables on creation if a checkpoint exists.
   checkpoint.restore(tf.train.latest_checkpoint(flags_obj.model_dir))