python - 无法保存和恢复经过训练的 TensorFlow 模型

Question

我刚刚阅读了Deep MNIST for Experts教程并修改了 mnist_deep.py代码以 saver = tf.train.Saver()在创建会话之前和 saver.save(sess, './mnist_deep_model', global_step=2000)在 for 循环训练模型之后使用来保存训练模型。由于我的工作文件夹中有以下四个文件，因此它似乎已正确保存：

• 检查点
• mnist_deep_model-2000.data-00000-of-00001
• mnist_deep_model-2000.indexs
• mnist_deep_model-2000.meta

我还修改了mnist_deep.py，添加了以下两个函数，以便能够在单个测试图像上一一测试模型：

def indexMax(list):
  """indexMax returns the index of the max element of the list."""
  return list.index(max(list))


def identifyDigitInImage(sess, x, y_conv, keep_prob, image):
  """identifyDigitInImage apply the trained model to given image to identify the represented digit."""
  result = sess.run(y_conv, {x:[image], keep_prob: 1.0})[0].tolist()
  return indexMax(result)

我还在主函数的末尾添加了一个循环，在该循环中，我在测试集中随机选择一个测试图像，并尝试使用此函数将训练好的模型应用于每个图像。它似乎有效，因为我在这个测试循环中获得了相同的准确度：99.2%

然后我编写了第二个程序：mnist_deep_restore_trained_model.py（也是基于 mnist_deep.py 源代码）试图恢复之前保存的训练模型并将测试图像应用于它期望获得相同的准确性。

当然，我从这个程序中删除了创建、训练和测试模型所需的所有代码（deepnn()函数和所有相关函数、张量创建：x = tf.placeholder(tf.float32, [None, 784])、y_conv、keep_prob = deepnn(x)、loss、optimizer和准确性的东西......），我只是恢复了保存的模型这样：（一旦会话打开）

saver = tf.train.import_meta_graph('./mnist_deep_model-2000.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))

我还在会话开始时删除了全局变量初始化，因为全局变量的值应该已经从训练模型中恢复：

但是，为了能够应用模型来识别给定测试图像的数字（cf function identifyDigitInImage(sess, x, y_conv, keep_prob, image)），我仍然需要获取张量变量x、y_conv 和 keep_prob。所以我在从磁盘恢复模型后添加了以下几行：

graph = tf.get_default_graph()
x = graph.get_tensor_by_name("x:0")
keep_prob = graph.get_tensor_by_name("keep_prob:0")
y_conv = graph.get_tensor_by_name("y_conv:0")

最后，我还在第二个程序的末尾添加了与 mnist_deep.py 中相同的测试循环，期望从这个恢复的模型中获得相同的结果......

不幸的是，我在第一次调用 get_tensor_by_name() 时遇到异常：

x = graph.get_tensor_by_name("x:0")
KeyError: "The name 'x:0' refers to a Tensor which does not exist. The operation 'x', does not exist in the graph."

其他get_tensor_by_name()调用也会引发同样的异常。

我究竟做错了什么 ？为什么不可能以这种方式获得这些张量？

这是我完整的mnist_deep.py源代码：

# 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.
# ==============================================================================

"""A deep MNIST classifier using convolutional layers.
See extensive documentation at
https://www.tensorflow.org/get_started/mnist/pros
"""
# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order

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

import argparse
import sys
import tempfile
import random

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None


def deepnn(x):
  """deepnn builds the graph for a deep net for classifying digits.
  Args:
    x: an input tensor with the dimensions (N_examples, 784), where 784 is the
    number of pixels in a standard MNIST image.
  Returns:
    A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
    equal to the logits of classifying the digit into one of 10 classes (the
    digits 0-9). keep_prob is a scalar placeholder for the probability of
    dropout.
  """
  # Reshape to use within a convolutional neural net.
  # Last dimension is for "features" - there is only one here, since images are
  # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
  with tf.name_scope('reshape'):
    x_image = tf.reshape(x, [-1, 28, 28, 1])

  # First convolutional layer - maps one grayscale image to 32 feature maps.
  with tf.name_scope('conv1'):
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

  # Pooling layer - downsamples by 2X.
  with tf.name_scope('pool1'):
    h_pool1 = max_pool_2x2(h_conv1)

  # Second convolutional layer -- maps 32 feature maps to 64.
  with tf.name_scope('conv2'):
    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)

  # Second pooling layer.
  with tf.name_scope('pool2'):
    h_pool2 = max_pool_2x2(h_conv2)

  # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
  # is down to 7x7x64 feature maps -- maps this to 1024 features.
  with tf.name_scope('fc1'):
    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)

  # Dropout - controls the complexity of the model, prevents co-adaptation of
  # features.
  with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

  # Map the 1024 features to 10 classes, one for each digit
  with tf.name_scope('fc2'):
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])

    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
  return y_conv, keep_prob


def conv2d(x, W):
  """conv2d returns a 2d convolution layer with full stride."""
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
  """max_pool_2x2 downsamples a feature map by 2X."""
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')


def weight_variable(shape):
  """weight_variable generates a weight variable of a given shape."""
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)


def bias_variable(shape):
  """bias_variable generates a bias variable of a given shape."""
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)


def indexMax(list):
  """indexMax returns the index of the max element of the list."""
  return list.index(max(list))


def identifyDigitInImage(sess, x, y_conv, keep_prob, image):
  """identifyDigitInImage apply the trained model to given image to identify the represented digit."""
  result = sess.run(y_conv, {x:[image], keep_prob: 1.0})[0].tolist()
  return indexMax(result)


def main(_):
  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  # Create the model
  x = tf.placeholder(tf.float32, [None, 784])

  # Define loss and optimizer
  y_ = tf.placeholder(tf.float32, [None, 10])

  # Build the graph for the deep net
  y_conv, keep_prob = deepnn(x)

  with tf.name_scope('loss'):
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
                                                            logits=y_conv)
  cross_entropy = tf.reduce_mean(cross_entropy)

  with tf.name_scope('adam_optimizer'):
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

  with tf.name_scope('accuracy'):
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    correct_prediction = tf.cast(correct_prediction, tf.float32)
  accuracy = tf.reduce_mean(correct_prediction)

  #graph_location = tempfile.mkdtemp()
  #print('Saving graph to: %s' % graph_location)
  #train_writer = tf.summary.FileWriter(graph_location)
  #train_writer.add_graph(tf.get_default_graph())

  # Prepare a saver to save the trained model:
  saver = tf.train.Saver()

  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    # Save the untrained model:
    saver.save(sess, './mnist_deep_model')

    # Train the model:
    for i in range(20000):
      batch = mnist.train.next_batch(50)
      if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={
            x: batch[0], y_: batch[1], keep_prob: 1.0})
        print('step %d, training accuracy %g' % (i, train_accuracy))
      train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    # Save the trained model:
    saver.save(sess, './mnist_deep_model', global_step=2000)

    # Display the test accuracy:
    print('test accuracy %g' % accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

    # Now try to apply the model to randomly choosen test images, one by one:
    stop = False
    count = 0
    ok_count = 0
    while not stop:
        # Choosing a test image index:
        test_image_index = random.randint(0, len(mnist.test.images) - 1)
        test_image = mnist.test.images[test_image_index]

        # Applying the trained model to identify the digit from the test image:
        identified_digit = identifyDigitInImage(sess, x, y_conv, keep_prob, test_image)

        # Display the identified digit:
        print("The written digit on the given image has been identified as a {}".format(identified_digit))

        # Check the expected_digit from the test label of the choosen test image:
        expected_digit = indexMax(mnist.test.labels[test_image_index].tolist())

        # Display the expected digit:
        print("Actually, the digit is a {}".format(expected_digit))

        # Count the correctly identified digits:
        if identified_digit == expected_digit:
            ok_count += 1

        # Stop the loop after 10000 iterations
        count += 1
        stop = count == 10000

        # Display the measured accuracy during the test loop:
    print("2nd Test accuracy = {}%".format(100 * (ok_count / count)))


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument('--data_dir', type=str,
                      default='/tmp/tensorflow/mnist/input_data',
                      help='Directory for storing input data')
  FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)  

这里是我完整的 mnist_deep_restore_trained_model.py源代码：

# 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.
# =============================================================================

"""A deep MNIST classifier using convolutional layers.
See extensive documentation at
https://www.tensorflow.org/get_started/mnist/pros
"""
# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order

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

import argparse
import sys
import random

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None

def indexMax(list):
  """indexMax returns the index of the max element of the list."""
  return list.index(max(list))


def identifyDigitInImage(sess, x, y_conv, keep_prob, image):
  """identifyDigitInImage apply the trained model to given image to identify the represented digit."""
  result = sess.run(y_conv, {x:[image], keep_prob: 1.0})[0].tolist()
  return indexMax(result)


def main(_):
  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  with tf.Session() as sess:

    # Restoring the trained model previously saved:
    saver = tf.train.import_meta_graph('./mnist_deep_model-2000.meta')
    saver.restore(sess, tf.train.latest_checkpoint('./'))

    # Trying to get back some required tensors variables from the restored graph:
    graph = tf.get_default_graph()
    x = graph.get_tensor_by_name("x:0")
    # This call fails with the following exception:
    # KeyError: "The name 'x:0' refers to a Tensor which does not exist. The operation 'x', does not exist in the graph."
    keep_prob = graph.get_tensor_by_name("keep_prob:0")
    y_conv = graph.get_tensor_by_name("y_conv:0")

    # Now try to apply the model to randomly choosen test images, one by one:
    stop = False
    count = 0
    ok_count = 0
    while not stop:
      # Choosing a test image index:
      test_image_index = random.randint(0, len(mnist.test.images) - 1)
      test_image = mnist.test.images[test_image_index]

      # Applying the trained model to identify the digit from the test image:
      identified_digit = identifyDigitInImage(sess, x, y_conv, keep_prob, test_image)

      # Display the identified digit:
      print("The written digit on the given image has been identified as a {}".format(identified_digit))

      # Check the expected_digit from the test label of the choosen test image:
      expected_digit = indexMax(mnist.test.labels[test_image_index].tolist())

      # Display the expected digit:
      print("Actually, the digit is a {}".format(expected_digit))

      # Count the correctly identified digits:
      if identified_digit == expected_digit:
        ok_count += 1

      # Stop the loop after 10000 iterations
      count += 1
      stop = count == 10000

    # Display the measured accuracy during the test loop:
    print("Test accuracy = {}%".format(100 * (ok_count / count)))


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument('--data_dir', type=str,
                      default='/tmp/tensorflow/mnist/input_data',
                      help='Directory for storing input data')
  FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

Answer 1

您没有为占位符提供明确的名称：

# Create the model
x = tf.placeholder(tf.float32, [None, 784])

# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])

...因此，它们被命名Placeholder并Placeholder_1在保存的图表中，因此出现错误。将此代码更改为：

# Create the model
x = tf.placeholder(tf.float32, [None, 784], name='x')

# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10], name='y')

...同样适用于keep_prob和y_conv（用于tf.add给+操作命名）。顺便说一句，命名所有变量和关键操作并使用范围总是一个好主意。重新训练模型后，您mnist_deep_restore_trained_model.py应该可以工作。

Answer 2

感谢您的帮助马克西姆。现在工作正常。

这是我固定的 mnist_deep.py 代码：

# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order

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

import argparse
import sys
import tempfile
import random

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None


def deepnn(x):
  """deepnn builds the graph for a deep net for classifying digits.
  Args:
    x: an input tensor with the dimensions (N_examples, 784), where 784 is the
    number of pixels in a standard MNIST image.
  Returns:
    A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
    equal to the logits of classifying the digit into one of 10 classes (the
    digits 0-9). keep_prob is a scalar placeholder for the probability of
    dropout.
  """
  # Reshape to use within a convolutional neural net.
  # Last dimension is for "features" - there is only one here, since images are
  # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
  with tf.name_scope('reshape'):
    x_image = tf.reshape(x, [-1, 28, 28, 1])

  # First convolutional layer - maps one grayscale image to 32 feature maps.
  with tf.name_scope('conv1'):
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

  # Pooling layer - downsamples by 2X.
  with tf.name_scope('pool1'):
    h_pool1 = max_pool_2x2(h_conv1)

  # Second convolutional layer -- maps 32 feature maps to 64.
  with tf.name_scope('conv2'):
    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)

  # Second pooling layer.
  with tf.name_scope('pool2'):
    h_pool2 = max_pool_2x2(h_conv2)

  # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
  # is down to 7x7x64 feature maps -- maps this to 1024 features.
  with tf.name_scope('fc1'):
    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)

  # Dropout - controls the complexity of the model, prevents co-adaptation of
  # features.
  with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32, name='keep_prob')
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

  # Map the 1024 features to 10 classes, one for each digit
  with tf.name_scope('fc2'):
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])

    y_conv = tf.add(tf.matmul(h_fc1_drop, W_fc2), b_fc2, name='y_conv')
  return y_conv, keep_prob


def conv2d(x, W):
  """conv2d returns a 2d convolution layer with full stride."""
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
  """max_pool_2x2 downsamples a feature map by 2X."""
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')


def weight_variable(shape):
  """weight_variable generates a weight variable of a given shape."""
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)


def bias_variable(shape):
  """bias_variable generates a bias variable of a given shape."""
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)


def indexMax(list):
  """indexMax returns the index of the max element of the list."""
  return list.index(max(list))


def identifyDigitInImage(sess, x, y_conv, keep_prob, image):
  """identifyDigitInImage apply the trained model to given image to identify the represented digit."""
  result = sess.run(y_conv, {x:[image], keep_prob: 1.0})[0].tolist()
  return indexMax(result)


def main(_):
  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  # Create the model
  x = tf.placeholder(tf.float32, [None, 784], name = 'x')

  # Define loss and optimizer
  y_ = tf.placeholder(tf.float32, [None, 10], name = 'y_')

  # Build the graph for the deep net
  y_conv, keep_prob = deepnn(x)

  with tf.name_scope('loss'):
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
                                                            logits=y_conv)
  cross_entropy = tf.reduce_mean(cross_entropy)

  with tf.name_scope('adam_optimizer'):
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

  with tf.name_scope('accuracy'):
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    correct_prediction = tf.cast(correct_prediction, tf.float32)
  accuracy = tf.reduce_mean(correct_prediction)

  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    # Train the model:
    for i in range(20000):
      batch = mnist.train.next_batch(50)
      if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={
            x: batch[0], y_: batch[1], keep_prob: 1.0})
        print('step %d, training accuracy %g' % (i, train_accuracy))
      train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    # Save the trained model:
    saver = tf.train.Saver()
    saver.save(sess, './mnist_deep_model', global_step=2000)

    # Display the test accuracy:
    print('test accuracy %g' % accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

    # Now try to apply the model to randomly choosen test images, one by one:
    count = 0
    ok_count = 0
    while count < 10000:
        # Choosing a test image index:
        test_image_index = random.randint(0, len(mnist.test.images) - 1)
        test_image = mnist.test.images[test_image_index]

        # Applying the trained model to identify the digit from the test image:
        identified_digit = identifyDigitInImage(sess, x, y_conv, keep_prob, test_image)

        # Display the identified digit:
        print("The written digit on the given image has been identified as a {}".format(identified_digit))

        # Check the expected_digit from the test label of the choosen test image:
        expected_digit = indexMax(mnist.test.labels[test_image_index].tolist())

        # Display the expected digit:
        print("Actually, the digit is a {}".format(expected_digit))

        # Count the correctly identified digits:
        if identified_digit == expected_digit:
            ok_count += 1

        # Stop the loop after 10000 iterations
        count += 1


        # Display the measured accuracy during the test loop:
    print("2nd Test accuracy = {}%".format(100 * (ok_count / count)))


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument('--data_dir', type=str,
                      default='/tmp/tensorflow/mnist/input_data',
                      help='Directory for storing input data')
  FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

以及对应的固定mnist_deep_restore_train_model.py代码：

# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order

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

import argparse
import sys
import random

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None

def indexMax(list):
  """indexMax returns the index of the max element of the list."""
  return list.index(max(list))


def identifyDigitInImage(sess, x, y_conv, keep_prob, image):
  """identifyDigitInImage apply the trained model to given image to identify the represented digit."""
  result = sess.run(y_conv, {x:[image], keep_prob: 1.0})[0].tolist()
  return indexMax(result)


def main(_):
  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  with tf.Session() as sess:

    # Restoring the trained model previously saved:
    saver = tf.train.import_meta_graph('./mnist_deep_model-2000.meta')
    saver.restore(sess, tf.train.latest_checkpoint('./'))

    # Trying to get back some required tensors variables from the restored graph:
    graph = tf.get_default_graph()
    x = graph.get_tensor_by_name("x:0")
    keep_prob = graph.get_tensor_by_name("dropout/keep_prob:0")
    y_conv = graph.get_tensor_by_name("fc2/y_conv:0")

    # Now try to apply the model to randomly choosen test images, one by one:
    count = 0
    ok_count = 0
    while count < 10000:
      # Choosing a test image index:
      test_image_index = random.randint(0, len(mnist.test.images) - 1)
      test_image = mnist.test.images[test_image_index]

      # Applying the trained model to identify the digit from the test image:
      identified_digit = identifyDigitInImage(sess, x, y_conv, keep_prob, test_image)

      # Display the identified digit:
      print("The written digit on the given image has been identified as a {}".format(identified_digit))

      # Check the expected_digit from the test label of the choosen test image:
      expected_digit = indexMax(mnist.test.labels[test_image_index].tolist())

      # Display the expected digit:
      print("Actually, the digit is a {}".format(expected_digit))

      # Count the correctly identified digits:
      if identified_digit == expected_digit:
        ok_count += 1

      # Stop the loop after 10000 iterations
      count += 1

    # Display the measured accuracy during the test loop:
    print("Test accuracy = {}%".format(100 * (ok_count / count)))


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument('--data_dir', type=str,
                      default='/tmp/tensorflow/mnist/input_data',
                      help='Directory for storing input data')
  FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)