Merge pull request #2981 from asimshankar/mnist

[mnist]: Tweaks

official/mnist/README.md

@@ -12,13 +12,6 @@ APIs.
 ## Setup
 
 To begin, you'll simply need the latest version of TensorFlow installed.
-
-First convert the MNIST data to TFRecord file format by running the following:
-
-```
-python convert_to_records.py
-```
-
 Then to train the model, run the following:
 
 ```

official/mnist/convert_to_records.py

-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Converts MNIST data to TFRecords file format with Example protos.
-
-To read about optimizations that can be applied to the input preprocessing
-stage, see: https://www.tensorflow.org/performance/performance_guide#input_pipeline_optimization.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import os
-import sys
-
-import tensorflow as tf
-
-from tensorflow.contrib.learn.python.learn.datasets import mnist
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--directory', type=str, default='/tmp/mnist_data',
-                    help='Directory to download data files and write the '
-                    'converted result.')
-
-parser.add_argument('--validation_size', type=int, default=0,
-                    help='Number of examples to separate from the training '
-                    'data for the validation set.')
-
-
-def _int64_feature(value):
-  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
-
-
-def _bytes_feature(value):
-  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
-
-
-def convert_to(dataset, name, directory):
-  """Converts a dataset to TFRecords."""
-  images = dataset.images
-  labels = dataset.labels
-  num_examples = dataset.num_examples
-
-  if images.shape[0] != num_examples:
-    raise ValueError('Images size %d does not match label size %d.' %
-                     (images.shape[0], num_examples))
-  rows = images.shape[1]
-  cols = images.shape[2]
-  depth = images.shape[3]
-
-  filename = os.path.join(directory, name + '.tfrecords')
-  print('Writing', filename)
-  writer = tf.python_io.TFRecordWriter(filename)
-  for index in range(num_examples):
-    image_raw = images[index].tostring()
-    example = tf.train.Example(features=tf.train.Features(feature={
-        'height': _int64_feature(rows),
-        'width': _int64_feature(cols),
-        'depth': _int64_feature(depth),
-        'label': _int64_feature(int(labels[index])),
-        'image_raw': _bytes_feature(image_raw)}))
-    writer.write(example.SerializeToString())
-  writer.close()
-
-
-def main(unused_argv):
-  # Get the data.
-  datasets = mnist.read_data_sets(FLAGS.directory,
-                                   dtype=tf.uint8,
-                                   reshape=False,
-                                   validation_size=FLAGS.validation_size)
-
-  # Convert to Examples and write the result to TFRecords.
-  convert_to(datasets.train, 'train', FLAGS.directory)
-  convert_to(datasets.validation, 'validation', FLAGS.directory)
-  convert_to(datasets.test, 'test', FLAGS.directory)
-
-
-if __name__ == '__main__':
-  tf.logging.set_verbosity(tf.logging.INFO)
-  FLAGS, unparsed = parser.parse_known_args()
-  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

official/mnist/mnist.py

@@ -22,75 +22,53 @@ import os
 import sys
 
 import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
 
 parser = argparse.ArgumentParser()
 
 # Basic model parameters.
-parser.add_argument('--batch_size', type=int, default=100,
+parser.add_argument(
+    '--batch_size',
+    type=int,
+    default=100,
     help='Number of images to process in a batch')
 
-parser.add_argument('--data_dir', type=str, default='/tmp/mnist_data',
-                    help='Path to the MNIST data directory.')
+parser.add_argument(
+    '--data_dir',
+    type=str,
+    default='/tmp/mnist_data',
+    help='Path to directory containing the MNIST dataset')
 
-parser.add_argument('--model_dir', type=str, default='/tmp/mnist_model',
+parser.add_argument(
+    '--model_dir',
+    type=str,
+    default='/tmp/mnist_model',
     help='The directory where the model will be stored.')
 
-parser.add_argument('--train_epochs', type=int, default=40,
-                    help='Number of epochs to train.')
+parser.add_argument(
+    '--train_epochs', type=int, default=40, help='Number of epochs to train.')
 
 parser.add_argument(
-    '--data_format', type=str, default=None,
+    '--data_format',
+    type=str,
+    default=None,
     choices=['channels_first', 'channels_last'],
     help='A flag to override the data format used in the model. channels_first '
     'provides a performance boost on GPU but is not always compatible '
     'with CPU. If left unspecified, the data format will be chosen '
     'automatically based on whether TensorFlow was built for CPU or GPU.')
 
-_NUM_IMAGES = {
-    'train': 50000,
-    'validation': 10000,
-}
-
-
-def input_fn(is_training, filename, batch_size=1, num_epochs=1):
-  """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."""
-    features = tf.parse_single_example(
-        serialized_example,
-        features={
-            'image_raw': tf.FixedLenFeature([], tf.string),
-            'label': tf.FixedLenFeature([], tf.int64),
-        })
-    image = tf.decode_raw(features['image_raw'], tf.uint8)
-    image.set_shape([28 * 28])
-
-    # Normalize the values of the image from the range [0, 255] to [-0.5, 0.5]
-    image = tf.cast(image, tf.float32) / 255 - 0.5
-    label = tf.cast(features['label'], tf.int32)
-    return image, tf.one_hot(label, 10)
-
-  dataset = tf.data.TFRecordDataset([filename])
 
-  # 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
-    # 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)
+def train_dataset(data_dir):
+  """Returns a tf.data.Dataset yielding (image, label) pairs for training."""
+  data = input_data.read_data_sets(data_dir, one_hot=True).train
+  return tf.data.Dataset.from_tensor_slices((data.images, data.labels))
 
-  # Map example_parser over dataset, and batch results by up to batch_size
-  dataset = dataset.map(example_parser).prefetch(batch_size)
-  dataset = dataset.batch(batch_size)
-  iterator = dataset.make_one_shot_iterator()
-  images, labels = iterator.get_next()
 
-  return images, labels
+def eval_dataset(data_dir):
+  """Returns a tf.data.Dataset yielding (image, label) pairs for evaluation."""
+  data = input_data.read_data_sets(data_dir, one_hot=True).test
+  return tf.data.Dataset.from_tensors((data.images, data.labels))
 
 
 def mnist_model(inputs, mode, data_format):
@@ -104,8 +82,8 @@ def mnist_model(inputs, mode, data_format):
     # When running on GPU, transpose the data from channels_last (NHWC) to
     # channels_first (NCHW) to improve performance.
     # See https://www.tensorflow.org/performance/performance_guide#data_formats
-    data_format = ('channels_first' if tf.test.is_built_with_cuda() else
-                   'channels_last')
+    data_format = ('channels_first'
+                   if tf.test.is_built_with_cuda() else 'channels_last')
 
   if data_format == 'channels_first':
     inputs = tf.transpose(inputs, [0, 3, 1, 2])
@@ -127,8 +105,8 @@ def mnist_model(inputs, mode, data_format):
   # First max pooling layer with a 2x2 filter and stride of 2
   # Input Tensor Shape: [batch_size, 28, 28, 32]
   # Output Tensor Shape: [batch_size, 14, 14, 32]
-  pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2,
-                                  data_format=data_format)
+  pool1 = tf.layers.max_pooling2d(
+      inputs=conv1, pool_size=[2, 2], strides=2, data_format=data_format)
 
   # Convolutional Layer #2
   # Computes 64 features using a 5x5 filter.
@@ -147,8 +125,8 @@ def mnist_model(inputs, mode, data_format):
   # Second max pooling layer with a 2x2 filter and stride of 2
   # Input Tensor Shape: [batch_size, 14, 14, 64]
   # Output Tensor Shape: [batch_size, 7, 7, 64]
-  pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2,
-                                  data_format=data_format)
+  pool2 = tf.layers.max_pooling2d(
+      inputs=conv2, pool_size=[2, 2], strides=2, data_format=data_format)
 
   # Flatten tensor into a batch of vectors
   # Input Tensor Shape: [batch_size, 7, 7, 64]
@@ -159,8 +137,7 @@ def mnist_model(inputs, mode, data_format):
   # Densely connected layer with 1024 neurons
   # Input Tensor Shape: [batch_size, 7 * 7 * 64]
   # Output Tensor Shape: [batch_size, 1024]
-  dense = tf.layers.dense(inputs=pool2_flat, units=1024,
-                          activation=tf.nn.relu)
+  dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
 
   # Add dropout operation; 0.6 probability that element will be kept
   dropout = tf.layers.dropout(
@@ -211,34 +188,37 @@ def mnist_model_fn(features, labels, mode, params):
 
 
 def main(unused_argv):
-  # Make sure that training and testing data have been converted.
-  train_file = os.path.join(FLAGS.data_dir, 'train.tfrecords')
-  test_file = os.path.join(FLAGS.data_dir, 'test.tfrecords')
-  assert (tf.gfile.Exists(train_file) and tf.gfile.Exists(test_file)), (
-      'Run convert_to_records.py first to convert the MNIST data to TFRecord '
-      'file format.')
-
   # Create the Estimator
   mnist_classifier = tf.estimator.Estimator(
-      model_fn=mnist_model_fn, model_dir=FLAGS.model_dir,
-      params={'data_format': FLAGS.data_format})
+      model_fn=mnist_model_fn,
+      model_dir=FLAGS.model_dir,
+      params={
+          'data_format': FLAGS.data_format
+      })
 
   # Set up training hook that logs the training accuracy every 100 steps.
-  tensors_to_log = {
-      'train_accuracy': 'train_accuracy'
-  }
+  tensors_to_log = {'train_accuracy': 'train_accuracy'}
   logging_hook = tf.train.LoggingTensorHook(
       tensors=tensors_to_log, every_n_iter=100)
 
   # Train the model
-  mnist_classifier.train(
-      input_fn=lambda: input_fn(
-          True, train_file, FLAGS.batch_size, FLAGS.train_epochs),
-      hooks=[logging_hook])
+  def train_input_fn():
+    # When choosing shuffle buffer sizes, larger sizes result in better
+    # randomness, while smaller sizes use less memory. MNIST is a small
+    # enough dataset that we can easily shuffle the full epoch.
+    dataset = train_dataset(FLAGS.data_dir)
+    dataset = dataset.shuffle(buffer_size=50000).batch(FLAGS.batch_size).repeat(
+        FLAGS.train_epochs)
+    (images, labels) = dataset.make_one_shot_iterator().get_next()
+    return (images, labels)
+
+  mnist_classifier.train(input_fn=train_input_fn, hooks=[logging_hook])
 
   # Evaluate the model and print results
-  eval_results = mnist_classifier.evaluate(
-      input_fn=lambda: input_fn(False, test_file, FLAGS.batch_size))
+  def eval_input_fn():
+    return eval_dataset(FLAGS.data_dir).make_one_shot_iterator().get_next()
+
+  eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
   print()
   print('Evaluation results:\n\t%s' % eval_results)