我們很多時候需要儲存tensorflow模型的pb檔案，這時用tf.graph_util.convert_variables_to_constants函式會非常方便。
1.訓練網路：fully_conected.py

import argparse
import os
import time

import tensorflow as tf

import datasets_mnist
# Basic model parameters as external flags.
FLAGS = None
NUM_CLASSES = 10

# The MNIST images are always 28x28 pixels.
 

IMAGE_SIZE = 28
IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE
def placeholder_inputs(batch_size):
  images_placeholder = tf.placeholder(tf.float32, shape=(batch_size,IMAGE_PIXELS))
  labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))
  return images_placeholder, labels_placeholder


def fill_feed_dict
 
(data_set, images_pl, labels_pl):

  images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size,FLAGS.fake_data)
  feed_dict_value = {
      images_pl: images_feed,
      labels_pl: labels_feed,
  }
  return feed_dict_value
def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME'
 
)
def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')
def inference(images):
  """Build the MNIST model up to where it may be used for inference.

  Args:
    images: Images placeholder, from inputs().
    hidden1_units: Size of the first hidden layer.
    hidden2_units: Size of the second hidden layer.

  Returns:
    softmax_linear: Output tensor with the computed logits.
  """

  W_conv1 = tf.Variable(tf.truncated_normal([5, 5, 1, 32],stddev=0.1))
  b_conv1 = tf.Variable(0.0,[32])
  x_image = tf.reshape(images, [-1,28,28,1])
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  h_pool1 = max_pool_2x2(h_conv1)
  W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 32, 64],stddev=0.1))
  b_conv2 = tf.Variable(0.0,[64])  
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  h_pool2 = max_pool_2x2(h_conv2)
  W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024],stddev=0.1))
  b_fc1 = tf.Variable(0.0,[1024])

  h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

#  keep_prob = tf.placeholder("float")
  h_fc1_drop = tf.nn.dropout(h_fc1, 0.5)
  W_fc2 = tf.Variable(tf.truncated_normal([1024, 10],stddev=0.1))
  b_fc2 = tf.Variable(0.0,[10])
  logits=tf.matmul(h_fc1_drop, W_fc2) + b_fc2
  return logits


def loss(logits, labels):

  labels = tf.to_int64(labels)
#  labels = tf.to_float(labels)
#  labels= tf.one_hot(labels, 10)
  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=labels, name='xentropy')
#  y_conv = tf.nn.softmax(logits)
#  cross_entropy = -tf.reduce_sum(labels*tf.log(y_conv))
  return tf.reduce_mean(cross_entropy, name='xentropy_mean')


def training(loss):

  optimizer = tf.train.AdamOptimizer()
  train_op = optimizer.minimize(loss)
  return train_op


def evaluation(logits, labels):

  labels1=tf.one_hot(labels,10)
  correct = tf.nn.in_top_k(logits, labels, 1)

  correct1 = tf.equal(tf.argmax(logits,1), tf.argmax(labels1,1))

  return tf.reduce_mean(tf.cast(correct, tf.float32)) ,tf.reduce_mean(tf.cast(correct1, tf.float32))





def do_eval(sess,
            eval_correct,
            images_placeholder,
            labels_placeholder,
            data_set):

  true_count = 0  # Counts the number of correct predictions.
  steps_per_epoch = data_set.num_examples // FLAGS.batch_size
  num_examples = steps_per_epoch * FLAGS.batch_size
  for step in range(steps_per_epoch):
    feed_dict_value = fill_feed_dict(data_set,
                               images_placeholder,
                               labels_placeholder)
    true_count += sess.run(eval_correct, feed_dict=feed_dict_value)
  precision = float(true_count) / num_examples

  print('  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' %
        (num_examples, true_count, precision))


def run_training(logits,labels_placeholder):

  if tf.gfile.Exists(FLAGS.log_dir):
    tf.gfile.DeleteRecursively(FLAGS.log_dir)
  tf.gfile.MakeDirs(FLAGS.log_dir)

  loss_value= loss(logits, labels_placeholder)
  tf.summary.scalar('loss', loss_value)

  train_op = training(loss_value)

  eval_correct,eval_correct1 = evaluation(logits, labels_placeholder)

  tf.summary.scalar('precision', eval_correct)

  summary = tf.summary.merge_all()

  init = tf.global_variables_initializer()

  sess = tf.Session()

  summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)

  sess.run(init)

  for step in range(FLAGS.max_steps):
    start_time = time.time()

    feed_dict_value = fill_feed_dict(train,
                             images_placeholder,
                             labels_placeholder)


    _, loss_value1,eval_correct_value,eval_correct_value1 = sess.run([train_op, loss_value,eval_correct,eval_correct],feed_dict=feed_dict_value)

    duration = time.time() - start_time

    if step % 100 == 0:

      print('Step %d: loss = %.2f (%.3f sec),precision=%.3f,%.3f' % (step, loss_value1, duration,eval_correct_value,eval_correct_value1))
      summary_str = sess.run(summary, feed_dict=feed_dict_value)
      summary_writer.add_summary(summary_str, step)
      summary_writer.flush()

  # Save a checkpoint and evaluate the model periodically.
    if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:

      saver.save(sess, checkpoint_file, global_step=step)
    # Evaluate against the training set.
      print('Training Data Eval:')
      do_eval(sess,
            eval_correct,
            images_placeholder,
            labels_placeholder,
            train)
    # Evaluate against the validation set.
      print('Validation Data Eval:')
      do_eval(sess,
            eval_correct,
            images_placeholder,
            labels_placeholder,
            validation)
    # Evaluate against the test set.
      print('Test Data Eval:')
      do_eval(sess,
            eval_correct,
            images_placeholder,
            labels_placeholder,
            test)

def run_testing():
  sess=tf.Session()
  saver.restore(sess, tf.train.latest_checkpoint('ckpt'))
  feed_dict_value=fill_feed_dict(test,images_placeholder,labels_placeholder)
  a,accuracy=evaluation(logits,labels_placeholder)
  accuracy_=sess.run(a,feed_dict=feed_dict_value)
  print('accuracy is %f'%accuracy_)


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--max_steps',
      type=int,
      default=500,
      help='Number of steps to run trainer.'
  )
  parser.add_argument(
      '--batch_size',
      type=int,
      default=100,
      help='Batch size.  Must divide evenly into the dataset sizes.'
  )
  parser.add_argument(
      '--input_data_dir',
      type=str,
      default=os.path.join('datasets'),
      help='Directory to put the input data.'
  )
  parser.add_argument(
      '--log_dir',
      type=str,
      default=os.path.join('log'),
      help='Directory to put the log data.'
  )
  parser.add_argument(
      '--fake_data',
      default=False,
      help='If true, uses fake data for unit testing.',
      action='store_true'
  )
  parser.add_argument(
      '--train',
      type=bool ,default=True  
          )
  parser.add_argument(
          '--test',type=bool,default=True)
  FLAGS, unparsed = parser.parse_known_args()
  checkpoint_file = os.path.join('log', 'model.ckpt')

  train ,validation,test = datasets_mnist.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data)
  # Generate placeholders for the images and labels.
  images_placeholder, labels_placeholder = placeholder_inputs(
    FLAGS.batch_size)

  # Build a Graph that computes predictions from the inference model.
  logits = inference(images_placeholder)
  saver = tf.train.Saver()
  if FLAGS.train:
    run_training(logits,labels_placeholder)
#    exit('Training finished')
#  run_testing()

2.匯出pb檔案：export.py

import fully_conected as model
import tensorflow as tf
def export_graph(model_name):
  graph = tf.Graph()
  with graph.as_default():
    input_image = tf.placeholder(tf.float32, shape=[None,28*28], name='inputdata')

    logits = model.inference(input_image)
    y_conv = tf.nn.softmax(logits,name='outputdata')
    restore_saver = tf.train.Saver()

  with tf.Session(graph=graph) as sess:
    sess.run(tf.global_variables_initializer())
    latest_ckpt = tf.train.latest_checkpoint('log')
    restore_saver.restore(sess, latest_ckpt)
    output_graph_def = tf.graph_util.convert_variables_to_constants(
        sess, graph.as_graph_def(), ['outputdata'])

#    tf.train.write_graph(output_graph_def, 'log', model_name, as_text=False)
    with tf.gfile.GFile('log/mnist.pb', "wb") as f:  
        f.write(output_graph_def.SerializeToString())  
export_graph('mnist.pb')

3.測試：test.py

from __future__ import absolute_import, unicode_literals
from datasets_mnist import read_data_sets
import tensorflow as tf

train,validation,test = read_data_sets("datasets/", one_hot=True)

with tf.Graph().as_default():
    output_graph_def = tf.GraphDef()
    output_graph_path = 'log/mnist.pb'
#    sess.graph.add_to_collection("input", mnist.test.images)

    with open(output_graph_path, "rb") as f:
        output_graph_def.ParseFromString(f.read())
        tf.import_graph_def(output_graph_def, name="")

    with tf.Session() as sess:

        tf.initialize_all_variables().run()
        input_x = sess.graph.get_tensor_by_name("inputdata:0")        

        output = sess.graph.get_tensor_by_name("outputdata:0")


        y_conv_2 = sess.run(output,{input_x:test.images})
        print( "y_conv_2", y_conv_2)

        # Test trained model
        #y__2 = tf.placeholder("float", [None, 10])
        y__2 = test.labels
        correct_prediction_2 = tf.equal(tf.argmax(y_conv_2, 1), tf.argmax(y__2, 1))
        print ("correct_prediction_2", correct_prediction_2 )
        accuracy_2 = tf.reduce_mean(tf.cast(correct_prediction_2, "float"))
        print ("accuracy_2", accuracy_2)

        print ("check accuracy %g" % accuracy_2.eval())

4.這裡用的資料是mnist資料，程式碼是：datasets_mnist.py

import gzip
import os
import numpy
from six.moves import xrange  # pylint: disable=redefined-builtin

from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import random_seed

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'


def _read32(bytestream):
  dt = numpy.dtype(numpy.uint32).newbyteorder('>')
  return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]


def extract_images(f):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].

  Args:
    f: A file object that can be passed into a gzip reader.

  Returns:
    data: A 4D uint8 numpy array [index, y, x, depth].

  Raises:
    ValueError: If the bytestream does not start with 2051.

  """
  print('Extracting', f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
    magic = _read32(bytestream)
    if magic != 2051:
      raise ValueError('Invalid magic number %d in MNIST image file: %s' %
                       (magic, f.name))
    num_images = _read32(bytestream)
    rows = _read32(bytestream)
    cols = _read32(bytestream)
    buf = bytestream.read(rows * cols * num_images)
    data = numpy.frombuffer(buf, dtype=numpy.uint8)
    data = data.reshape(num_images, rows, cols, 1)
    return data


def dense_to_one_hot(labels_dense, num_classes):
  """Convert class labels from scalars to one-hot vectors."""
  num_labels = labels_dense.shape[0]
  index_offset = numpy.arange(num_labels) * num_classes
  labels_one_hot = numpy.zeros((num_labels, num_classes))
  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
  return labels_one_hot

#
def extract_labels(f, one_hot=False, num_classes=10):
  """Extract the labels into a 1D uint8 numpy array [index].

  Args:
    f: A file object that can be passed into a gzip reader.
    one_hot: Does one hot encoding for the result.
    num_classes: Number of classes for the one hot encoding.

  Returns:
    labels: a 1D uint8 numpy array.

  Raises:
    ValueError: If the bystream doesn't start with 2049.
  """
  print('Extracting', f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
    magic = _read32(bytestream)
    if magic != 2049:
      raise ValueError('Invalid magic number %d in MNIST label file: %s' %
                       (magic, f.name))
    num_items = _read32(bytestream)
    buf = bytestream.read(num_items)
    labels = numpy.frombuffer(buf, dtype=numpy.uint8)
    if one_hot:
      return dense_to_one_hot(labels, num_classes)
    return labels

#
class DataSet(object):

  def __init__(self,
               images,
               labels,
               fake_data=False,
               one_hot=False,
               dtype=dtypes.float32,
               reshape=True,
               seed=None):
    """Construct a DataSet.
    one_hot arg is used only if fake_data is true.  `dtype` can be either
    `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
    `[0, 1]`.  Seed arg provides for convenient deterministic testing.
    """
    seed1, seed2 = random_seed.get_seed(seed)
    # If op level seed is not set, use whatever graph level seed is returned
    numpy.random.seed(seed1 if seed is None else seed2)
    dtype = dtypes.as_dtype(dtype).base_dtype
    if dtype not in (dtypes.uint8, dtypes.float32):
      raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
                      dtype)
    if fake_data:
      self._num_examples = 10000
      self.one_hot = one_hot
    else:
      assert images.shape[0] == labels.shape[0], (
          'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))
      self._num_examples = images.shape[0]

      # Convert shape from [num examples, rows, columns, depth]
      # to [num examples, rows*columns] (assuming depth == 1)
      if reshape:
        assert images.shape[3] == 1
        images = images.reshape(images.shape[0],
                                images.shape[1] * images.shape[2])
      if dtype == dtypes.float32:
        # Convert from [0, 255] -> [0.0, 1.0].
        images = images.astype(numpy.float32)
        images = numpy.multiply(images, 1.0 / 255.0)
    self._images = images
    self._labels = labels
    self._epochs_completed = 0
    self._index_in_epoch = 0

  @property
  def images(self):
    return self._images

  @property
  def labels(self):
    return self._labels

  @property
  def num_examples(self):
    return self._num_examples

  @property
  def epochs_completed(self):
    return self._epochs_completed

  def next_batch(self, batch_size, fake_data=False, shuffle=True):
    """Return the next `batch_size` examples from this data set."""
    if fake_data:
      fake_image = [1] * 784
      if self.one_hot:
        fake_label = [1] + [0] * 9
      else:
        fake_label = 0
      return [fake_image for _ in xrange(batch_size)], [
          fake_label for _ in xrange(batch_size)
      ]
    start = self._index_in_epoch
    # Shuffle for the first epoch
    if self._epochs_completed == 0 and start == 0 and shuffle:
      perm0 = numpy.arange(self._num_examples)
      numpy.random.shuffle(perm0)
      self._images = self.images[perm0]
      self._labels = self.labels[perm0]
    # Go to the next epoch
    if start + batch_size > self._num_examples:
      # Finished epoch
      self._epochs_completed += 1
      # Get the rest examples in this epoch
      rest_num_examples = self._num_examples - start
      images_rest_part = self._images[start:self._num_examples]
      labels_rest_part = self._labels[start:self._num_examples]
      # Shuffle the data
      if shuffle:
        perm = numpy.arange(self._num_examples)
        numpy.random.shuffle(perm)
        self._images = self.images[perm]
        self._labels = self.labels[perm]
      # Start next epoch
      start = 0
      self._index_in_epoch = batch_size - rest_num_examples
      end = self._index_in_epoch
      images_new_part = self._images[start:end]
      labels_new_part = self._labels[start:end]
      return numpy.concatenate((images_rest_part, images_new_part), axis=0) , numpy.concatenate((labels_rest_part, labels_new_part), axis=0)
    else:
      self._index_in_epoch += batch_size
      end = self._index_in_epoch
      return self._images[start:end], self._labels[start:end]


def read_data_sets(train_dir,
                   fake_data=False,
                   one_hot=False,
                   dtype=dtypes.float32,
                   reshape=True,
                   validation_size=5000,
                   seed=None):
  if fake_data:

    def fake():
      return DataSet(
          [], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed)

    train = fake()
    validation = fake()
    test = fake()
    return base.Datasets(train=train, validation=validation, test=test)

#  TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
#  TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
#  TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
#  TEST_LABELS = 't10k-labels-idx1-ubyte.gz'

  local_file = os.path.join('datasets','train-images-idx3-ubyte.gz')
  with open(local_file, 'rb') as f:
    train_images = extract_images(f)

  local_file = os.path.join('datasets','train-labels-idx1-ubyte.gz')
  with open(local_file, 'rb') as f:
    train_labels = extract_labels(f, one_hot=one_hot)

  local_file = os.path.join('datasets','t10k-images-idx3-ubyte.gz')
  with open(local_file, 'rb') as f:
    test_images = extract_images(f)

  local_file =os.path.join('datasets','t10k-labels-idx1-ubyte.gz')
  with open(local_file, 'rb') as f:
    test_labels = extract_labels(f, one_hot=one_hot)

  if not 0 <= validation_size <= len(train_images):
    raise ValueError(
        'Validation size should be between 0 and {}. Received: {}.'
        .format(len(train_images), validation_size))

  validation_images = train_images[:validation_size]
  validation_labels = train_labels[:validation_size]
  train_images = train_images[validation_size:]
  train_labels = train_labels[validation_size:]

  train = DataSet(
      train_images, train_labels, dtype=dtype, reshape=reshape, seed=seed)
  validation = DataSet(
      validation_images,
      validation_labels,
      dtype=dtype,
      reshape=reshape,
      seed=seed)
  test = DataSet(
      test_images, test_labels, dtype=dtype, reshape=reshape, seed=seed)
  return train,validation,test