无法保存和恢复经过训练的 TensorFlow 模型
Unable to save and restore a trained TensorFlow Model
我刚刚阅读了 Deep MNIST for Experts 教程并修改了 mnist_deep.py 代码以使用保存训练模型
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。它似乎已正确保存，因为我的工作文件夹中有以下四个文件：
- 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张一张地测试模型：
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，在这个循环中，我在测试集中随机选择一个测试图像，并尝试使用这个函数将训练好的模型应用于每个图像。它似乎有效，因为我在此测试循环中获得了相同的准确度：99.2%
然后我写了第二个程序：mnist_deep_restore_trained_model.py（也是基于mnist_deep.py源码）试图恢复之前保存的训练好的模型并应用测试图像，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, 和准确率的东西...）我只是这样恢复保存的模型：（一旦会话打开）
saver = tf.train.import_meta_graph('./mnist_deep_model-2000.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))
我还删除了会话开始时的全局变量初始化，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，希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。
我做错了什么？为什么不能以这种方式获得这些张量？
这是我的完整 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)
您没有为占位符指定明确的名称：
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
... 结果，它们在保存的图表中被命名为 Placeholder 和 Placeholder_1，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')
... keep_prob 和 y_conv 也是如此（使用 tf.add to give a name to the + op). By the way, it's always a good idea to name all your variables and key operations and also use scopes。重新训练模型后，您的 mnist_deep_restore_trained_model.py 应该可以工作。
感谢马克西姆的帮助。现在一切正常。
这是我的固定 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)
我刚刚阅读了 Deep MNIST for Experts 教程并修改了 mnist_deep.py 代码以使用保存训练模型
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。它似乎已正确保存，因为我的工作文件夹中有以下四个文件：
- 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张一张地测试模型：
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，在这个循环中，我在测试集中随机选择一个测试图像，并尝试使用这个函数将训练好的模型应用于每个图像。它似乎有效，因为我在此测试循环中获得了相同的准确度：99.2%
然后我写了第二个程序：mnist_deep_restore_trained_model.py（也是基于mnist_deep.py源码）试图恢复之前保存的训练好的模型并应用测试图像，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, 和准确率的东西...）我只是这样恢复保存的模型：（一旦会话打开）
saver = tf.train.import_meta_graph('./mnist_deep_model-2000.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))
我还删除了会话开始时的全局变量初始化，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，希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。
我做错了什么？为什么不能以这种方式获得这些张量？
这是我的完整 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)
您没有为占位符指定明确的名称：
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
... 结果，它们在保存的图表中被命名为 Placeholder 和 Placeholder_1，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')
... keep_prob 和 y_conv 也是如此（使用 tf.add to give a name to the + op). By the way, it's always a good idea to name all your variables and key operations and also use scopes。重新训练模型后，您的 mnist_deep_restore_trained_model.py 应该可以工作。
感谢马克西姆的帮助。现在一切正常。
这是我的固定 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)