I just read the Deep MNIST for Experts tutorial and modified the mnist_deep.py code to save the trained model using
saver = tf.train.Saver() before creating the session and
saver.save(sess, './mnist_deep_model', global_step=2000) after the for loop training the model. It appears to be correctly saved since I got the four following files in my working folder:
I also modified the mnist_deep.py adding the two following functions to be able to test the model on individual test images, one by one:
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)
I also added at the end of the main function a loop in which I randomly choose one test image in the test set and try to apply the trained model to each of them with this function. It seems to work since I get the same accuracy in this test loop: 99.2%
Then I wrote a second program: mnist_deep_restore_trained_model.py (also based on the mnist_deep.py source code) trying to restore the previously saved trained model and apply test images to it expecting to get the same accuracy.
Of course, I removed of this program all the code required to create, train and test the model (the deepnn() function and all related functions, tensors creation: x = tf.placeholder(tf.float32, [None, 784]), y_conv, keep_prob = deepnn(x), loss, optimizer, and accuracy stuff...) and I simply restored the saved model this way: (once the session is open)
saver = tf.train.import_meta_graph('./mnist_deep_model-2000.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))
I also removed the global variables initialization at the beginning of the session since the values of the global variables should have been restored from the trained model:
But, to be able to apply the model in order to identify the digit of a given test image (cf function identifyDigitInImage(sess, x, y_conv, keep_prob, image)), I still need to get the Tensor variables x, y_conv and keep_prob. So I added the following lines after having restored the model from disk:
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")
Finally, I also added at the end of this second program the same test loop as in the mnist_deep.py expecting to get the same results from this restored model...
Unfortunately, I get an exception on the first call to get_tensor_by_name():
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."
The other get_tensor_by_name() calls would also raise this same exception.
What am I doing wrong ? Why is that not possible to get these Tensors this way ?
Here is my full mnist_deep.py source code:
# 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)
And here my full mnist_deep_restore_trained_model.py source code:
# 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)
You didn't give explicit names to your placeholders:
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
... as a result, they are named Placeholder and Placeholder_1 in the saved graph, hence the error. Change this code to:
# 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')
... and likewise for keep_prob and y_conv (use 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. After you retrain the model, your mnist_deep_restore_trained_model.py should work.