pbファイルを保存し、テストを呼び出す方法

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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

from __future__ import absolute_import, unicode_literals
from datasets_mnist import read_data_sets
import tensorflow as tf
import shutil
import os.path

export_dir = 'log/'

if os.path.exists(export_dir):
    shutil.rmtree(export_dir)

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

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')

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

g = tf.Graph()
with g.as_default():
    x = tf.placeholder("float", shape=[None, 784])
    y_ = tf.placeholder("float", shape=[None, 10])

    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    x_image = tf.reshape(x, [-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 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([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, keep_prob)

    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])

    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

    cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

    sess = tf.Session()
    sess.run(tf.initialize_all_variables())

    for i in range(2001):
        batch = train.next_batch(100)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(
                {x: batch[0], y_: batch[1], keep_prob: 1.0}, sess)
            print ("step %d, training accuracy %g" % (i, train_accuracy))
        train_step.run(
            {x: batch[0], y_: batch[1], keep_prob: 0.5}, sess)

    print( "test accuracy %g" % accuracy.eval(
        {x: test.images, y_: test.labels, keep_prob: 1.0}, sess))

# Store variable
_W_conv1 = W_conv1.eval(sess)
_b_conv1 = b_conv1.eval(sess)
_W_conv2 = W_conv2.eval(sess)
_b_conv2 = b_conv2.eval(sess)
_W_fc1 = W_fc1.eval(sess)
_b_fc1 = b_fc1.eval(sess)
_W_fc2 = W_fc2.eval(sess)
_b_fc2 = b_fc2.eval(sess)

sess.close()

# Create new graph for exporting
g_2 = tf.Graph()
with g_2.as_default():
    x_2 = tf.placeholder("float", shape=[None, 784], name="inputdata")
    W_conv1_2 = tf.constant(_W_conv1, name="constant_W_conv1")
    b_conv1_2 = tf.constant(_b_conv1, name="constant_b_conv1")
    x_image_2 = tf.reshape(x_2, [-1, 28, 28, 1])
    h_conv1_2 = tf.nn.relu(conv2d(x_image_2, W_conv1_2) + b_conv1_2)
    h_pool1_2 = max_pool_2x2(h_conv1_2)

    W_conv2_2 = tf.constant(_W_conv2, name="constant_W_conv2")
    b_conv2_2 = tf.constant(_b_conv2, name="constant_b_conv2")
    h_conv2_2 = tf.nn.relu(conv2d(h_pool1_2, W_conv2_2) + b_conv2_2)
    h_pool2_2 = max_pool_2x2(h_conv2_2)

    W_fc1_2 = tf.constant(_W_fc1, name="constant_W_fc1")
    b_fc1_2 = tf.constant(_b_fc1, name="constanexport_dirt_b_fc1")
    h_pool2_flat_2 = tf.reshape(h_pool2_2, [-1, 7 * 7 * 64])
    h_fc1_2 = tf.nn.relu(tf.matmul(h_pool2_flat_2, W_fc1_2) + b_fc1_2)

    W_fc2_2 = tf.constant(_W_fc2, name="constant_W_fc2")
    b_fc2_2 = tf.constant(_b_fc2, name="constant_b_fc2")

    # DropOut is skipped for exported graph.

    y_conv_2 = tf.nn.softmax(tf.matmul(h_fc1_2, W_fc2_2) + b_fc2_2, name="outputdata")

    sess_2 = tf.Session()
    init_2 = tf.initialize_all_variables();
    sess_2.run(init_2)

    graph_def = g_2.as_graph_def()
    tf.train.write_graph(graph_def, export_dir, 'expert-graph.weights', as_text=False)

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

    print( "check accuracy %g" % accuracy_2.eval(
        {x_2: test.images, y__2: test.labels}, sess_2))

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/expert-graph.weights'
#    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")        
        print( input_x)
        output = sess.graph.get_tensor_by_name("outputdata:0")
        print( output)

        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())