Changing tf.contrib.data to tf.data for release of tf 1.4

official/mnist/mnist.py

@@ -53,7 +53,7 @@ _NUM_IMAGES = {
 
 
 def input_fn(is_training, filename, batch_size=1, num_epochs=1):
-  """A simple input_fn using the contrib.data input pipeline."""
+  """A simple input_fn using the tf.data input pipeline."""
 
   def example_parser(serialized_example):
     """Parses a single tf.Example into image and label tensors."""
@@ -71,8 +71,12 @@ def input_fn(is_training, filename, batch_size=1, num_epochs=1):
     label = tf.cast(features['label'], tf.int32)
     return image, tf.one_hot(label, 10)
 
-  dataset = tf.contrib.data.TFRecordDataset([filename])
+  dataset = tf.data.TFRecordDataset([filename])
 
+  # Parse each example in the dataset
+  dataset = dataset.map(example_parser)
+
+  # Apply dataset transformations
   if is_training:
     # When choosing shuffle buffer sizes, larger sizes result in better
     # randomness, while smaller sizes have better performance. Because MNIST is

official/resnet/cifar10_main.py

@@ -77,7 +77,7 @@ _SHUFFLE_BUFFER = 20000
 def record_dataset(filenames):
   """Returns an input pipeline Dataset from `filenames`."""
   record_bytes = _HEIGHT * _WIDTH * _DEPTH + 1
-  return tf.contrib.data.FixedLengthRecordDataset(filenames, record_bytes)
+  return tf.data.FixedLengthRecordDataset(filenames, record_bytes)
 
 
 def get_filenames(is_training, data_dir):
@@ -138,7 +138,7 @@ def train_preprocess_fn(image, label):
 
 
 def input_fn(is_training, data_dir, batch_size, num_epochs=1):
-  """Input_fn using the contrib.data input pipeline for CIFAR-10 dataset.
+  """Input_fn using the tf.data input pipeline for CIFAR-10 dataset.
 
   Args:
     is_training: A boolean denoting whether the input is for training.
@@ -148,13 +148,11 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1):
     A tuple of images and labels.
   """
   dataset = record_dataset(get_filenames(is_training, data_dir))
-  dataset = dataset.map(dataset_parser, num_threads=1,
-                        output_buffer_size=2 * batch_size)
+  dataset = dataset.map(dataset_parser)
 
   # For training, preprocess the image and shuffle.
   if is_training:
-    dataset = dataset.map(train_preprocess_fn, num_threads=1,
-                          output_buffer_size=2 * batch_size)
+    dataset = dataset.map(train_preprocess_fn)
 
     # When choosing shuffle buffer sizes, larger sizes result in better
     # randomness, while smaller sizes have better performance.
@@ -162,9 +160,7 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1):
 
   # Subtract off the mean and divide by the variance of the pixels.
   dataset = dataset.map(
-      lambda image, label: (tf.image.per_image_standardization(image), label),
-      num_threads=1,
-      output_buffer_size=2 * batch_size)
+      lambda image, label: (tf.image.per_image_standardization(image), label))
 
   # We call repeat after shuffling, rather than before, to prevent separate
   # epochs from blending together.

official/resnet/imagenet_main.py

@@ -134,17 +134,16 @@ def dataset_parser(value, is_training):
 
 def input_fn(is_training, data_dir, batch_size, num_epochs=1):
   """Input function which provides batches for train or eval."""
-  dataset = tf.contrib.data.Dataset.from_tensor_slices(
+  dataset = tf.data.Dataset.from_tensor_slices(
       filenames(is_training, data_dir))
 
   if is_training:
     dataset = dataset.shuffle(buffer_size=_FILE_SHUFFLE_BUFFER)
 
-  dataset = dataset.flat_map(tf.contrib.data.TFRecordDataset)
+  dataset = dataset.flat_map(tf.data.TFRecordDataset)
 
   dataset = dataset.map(lambda value: dataset_parser(value, is_training),
-                        num_threads=5,
-                        output_buffer_size=batch_size)
+                        num_parallel_calls=5)
 
   if is_training:
     # When choosing shuffle buffer sizes, larger sizes result in better

official/wide_deep/wide_deep.py

@@ -178,8 +178,8 @@ def input_fn(data_file, num_epochs, shuffle, batch_size):
     return features, tf.equal(labels, '>50K')
 
   # Extract lines from input files using the Dataset API.
-  dataset = tf.contrib.data.TextLineDataset(data_file)
-  dataset = dataset.map(parse_csv, num_threads=5)
+  dataset = tf.data.TextLineDataset(data_file)
+  dataset = dataset.map(parse_csv, num_parallel_calls=5)
 
   if shuffle:
     dataset = dataset.shuffle(buffer_size=_SHUFFLE_BUFFER)
@@ -193,7 +193,6 @@ def input_fn(data_file, num_epochs, shuffle, batch_size):
   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)