Input improvements

official/mnist/mnist.py

@@ -79,13 +79,16 @@ def example_parser(serialized_example):
     # a small dataset, we can easily shuffle the full epoch.
     dataset = dataset.shuffle(buffer_size=_NUM_IMAGES['train'])
 
+  # We call repeat after shuffling, rather than before, to prevent separate
+  # epochs from blending together.
   dataset = dataset.repeat(num_epochs)
 
   # Map example_parser over dataset, and batch results by up to batch_size
   dataset = dataset.map(
       example_parser, num_threads=1, output_buffer_size=batch_size)
   dataset = dataset.batch(batch_size)
-  images, labels = dataset.make_one_shot_iterator().get_next()
+  iterator = dataset.make_one_shot_iterator()
+  images, labels = iterator.get_next()
 
   return images, labels
 

official/resnet/cifar10_main.py

@@ -166,11 +166,14 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1):
       num_threads=1,
       output_buffer_size=2 * batch_size)
 
+  # We call repeat after shuffling, rather than before, to prevent separate
+  # epochs from blending together.
   dataset = dataset.repeat(num_epochs)
 
   # Batch results by up to batch_size, and then fetch the tuple from the
   # iterator.
-  iterator = dataset.batch(batch_size).make_one_shot_iterator()
+  dataset = dataset.batch(batch_size)
+  iterator = dataset.make_one_shot_iterator()
   images, labels = iterator.get_next()
 
   return images, labels

official/resnet/imagenet_main.py

@@ -73,6 +73,7 @@
     'validation': 50000,
 }
 
+_FILE_SHUFFLE_BUFFER = 1024
 _SHUFFLE_BUFFER = 1500
 
 
@@ -81,11 +82,11 @@ def filenames(is_training, data_dir):
   if is_training:
     return [
         os.path.join(data_dir, 'train-%05d-of-01024' % i)
-        for i in range(0, 1024)]
+        for i in range(1024)]
   else:
     return [
         os.path.join(data_dir, 'validation-%05d-of-00128' % i)
-        for i in range(0, 128)]
+        for i in range(128)]
 
 
 def dataset_parser(value, is_training):
@@ -137,11 +138,9 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1):
       filenames(is_training, data_dir))
 
   if is_training:
-    dataset = dataset.shuffle(buffer_size=1024)
-  dataset = dataset.flat_map(tf.contrib.data.TFRecordDataset)
+    dataset = dataset.shuffle(buffer_size=_FILE_SHUFFLE_BUFFER)
 
-  if is_training:
-    dataset = dataset.repeat(num_epochs)
+  dataset = dataset.flat_map(tf.contrib.data.TFRecordDataset)
 
   dataset = dataset.map(lambda value: dataset_parser(value, is_training),
                         num_threads=5,
@@ -152,7 +151,12 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1):
     # randomness, while smaller sizes have better performance.
     dataset = dataset.shuffle(buffer_size=_SHUFFLE_BUFFER)
 
-  iterator = dataset.batch(batch_size).make_one_shot_iterator()
+  # We call repeat after shuffling, rather than before, to prevent separate
+  # epochs from blending together.
+  dataset = dataset.repeat(num_epochs)
+  dataset = dataset.batch(batch_size)
+
+  iterator = dataset.make_one_shot_iterator()
   images, labels = iterator.get_next()
   return images, labels
 
@@ -188,8 +192,8 @@ def resnet_model_fn(features, labels, mode, params):
       [tf.nn.l2_loss(v) for v in tf.trainable_variables()])
 
   if mode == tf.estimator.ModeKeys.TRAIN:
-    # Scale the learning rate linearly with the batch size. When the batch size is
-    # 256, the learning rate should be 0.1.
+    # Scale the learning rate linearly with the batch size. When the batch size
+    # is 256, the learning rate should be 0.1.
     initial_learning_rate = 0.1 * params['batch_size'] / 256
     batches_per_epoch = _NUM_IMAGES['train'] / params['batch_size']
     global_step = tf.train.get_or_create_global_step()

official/wide_deep/wide_deep.py

@@ -61,6 +61,8 @@
     '--test_data', type=str, default='/tmp/census_data/adult.test',
     help='Path to the test data.')
 
+_SHUFFLE_BUFFER = 100000
+
 
 def build_model_columns():
   """Builds a set of wide and deep feature columns."""
@@ -167,6 +169,7 @@ def input_fn(data_file, num_epochs, shuffle, batch_size):
   assert tf.gfile.Exists(data_file), (
       '%s not found. Please make sure you have either run data_download.py or '
       'set both arguments --train_data and --test_data.' % data_file)
+
   def parse_csv(value):
     print('Parsing', data_file)
     columns = tf.decode_csv(value, record_defaults=_CSV_COLUMN_DEFAULTS)
@@ -178,49 +181,36 @@ def parse_csv(value):
   dataset = tf.contrib.data.TextLineDataset(data_file)
   dataset = dataset.map(parse_csv, num_threads=5)
 
-  # Apply transformations to the Dataset
-  dataset = dataset.batch(batch_size)
+  if shuffle:
+    dataset = dataset.shuffle(buffer_size=_SHUFFLE_BUFFER)
+
+  # We call repeat after shuffling, rather than before, to prevent separate
+  # epochs from blending together.
   dataset = dataset.repeat(num_epochs)
+  dataset = dataset.batch(batch_size)
 
-  # Input function that is called by the Estimator
-  def _input_fn():
-    if shuffle:
-      # Apply shuffle transformation to re-shuffle the dataset in each call.
-      shuffled_dataset = dataset.shuffle(buffer_size=100000)
-      iterator = shuffled_dataset.make_one_shot_iterator()
-    else:
-      iterator = dataset.make_one_shot_iterator()
-    features, labels = iterator.get_next()
-    return features, labels
-  return _input_fn
+  iterator = dataset.make_one_shot_iterator()
+  features, labels = iterator.get_next()
+  return features, labels
 
 
 def main(unused_argv):
   # Clean up the model directory if present
   shutil.rmtree(FLAGS.model_dir, ignore_errors=True)
-
   model = build_estimator(FLAGS.model_dir, FLAGS.model_type)
 
-  # Set up input function generators for the train and test data files.
-  train_input_fn = input_fn(
-      data_file=FLAGS.train_data,
-      num_epochs=FLAGS.epochs_per_eval,
-      shuffle=True,
-      batch_size=FLAGS.batch_size)
-  eval_input_fn = input_fn(
-      data_file=FLAGS.test_data,
-      num_epochs=1,
-      shuffle=False,
-      batch_size=FLAGS.batch_size)
-
   # Train and evaluate the model every `FLAGS.epochs_per_eval` epochs.
   for n in range(FLAGS.train_epochs // FLAGS.epochs_per_eval):
-    model.train(input_fn=train_input_fn)
-    results = model.evaluate(input_fn=eval_input_fn)
+    model.train(input_fn=lambda: input_fn(
+        FLAGS.train_data, FLAGS.epochs_per_eval, True, FLAGS.batch_size))
+
+    results = model.evaluate(input_fn=lambda: input_fn(
+        FLAGS.test_data, 1, False, FLAGS.batch_size))
 
     # Display evaluation metrics
     print('Results at epoch', (n + 1) * FLAGS.epochs_per_eval)
     print('-' * 30)
+
     for key in sorted(results):
       print('%s: %s' % (key, results[key]))
 

official/wide_deep/wide_deep_test.py

@@ -54,7 +54,7 @@ def setUp(self):
       temp_csv.write(TEST_INPUT)
 
   def test_input_fn(self):
-    features, labels = wide_deep.input_fn(self.input_csv, 1, False, 1)()
+    features, labels = wide_deep.input_fn(self.input_csv, 1, False, 1)
     with tf.Session() as sess:
       features, labels = sess.run((features, labels))
 
@@ -78,20 +78,20 @@ def build_and_test_estimator(self, model_type):
 
     # Train for 1 step to initialize model and evaluate initial loss
     model.train(
-        input_fn=wide_deep.input_fn(
+        input_fn=lambda: wide_deep.input_fn(
             TEST_CSV, num_epochs=1, shuffle=True, batch_size=1),
         steps=1)
     initial_results = model.evaluate(
-        input_fn=wide_deep.input_fn(
+        input_fn=lambda: wide_deep.input_fn(
             TEST_CSV, num_epochs=1, shuffle=False, batch_size=1))
 
     # Train for 40 steps at batch size 2 and evaluate final loss
     model.train(
-        input_fn=wide_deep.input_fn(
+        input_fn=lambda: wide_deep.input_fn(
             TEST_CSV, num_epochs=None, shuffle=True, batch_size=2),
         steps=40)
     final_results = model.evaluate(
-        input_fn=wide_deep.input_fn(
+        input_fn=lambda: wide_deep.input_fn(
             TEST_CSV, num_epochs=1, shuffle=False, batch_size=1))
 
     print('%s initial results:' % model_type, initial_results)