Python 无法保存和还原经过训练的TensorFlow模型
我刚刚阅读了教程,并修改了mnist\u deep.py代码,以使用Python 无法保存和还原经过训练的TensorFlow模型,python,tensorflow,conv-neural-network,restore,machine-learning-model,Python,Tensorflow,Conv Neural Network,Restore,Machine Learning Model,我刚刚阅读了教程,并修改了mnist\u deep.py代码,以使用 saver=tf.train.saver()在创建会话和 saver.save(sess,./mnist\u deep\u model',global\u step=2000)在对模型进行循环训练之后。由于我在工作文件夹中获得了以下四个文件,因此它似乎已正确保存: 检查站 mnist_deep_model-2000.data-00000-of-00001 mnist_deep_model-2000.indexs mnist_
saver=tf.train.saver()
在创建会话和
saver.save(sess,./mnist\u deep\u model',global\u step=2000)
在对模型进行循环训练之后。由于我在工作文件夹中获得了以下四个文件,因此它似乎已正确保存:
- 检查站
- mnist_deep_model-2000.data-00000-of-00001
- mnist_deep_model-2000.indexs
- mnist_deep_model-2000.meta
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,并尝试使用该函数将经过训练的模型应用于每个图像。这似乎是可行的,因为我在这个测试循环中获得了相同的精度:99.2%
然后,我编写了第二个程序:mnist\u deep\u restore\u trained\u model.py(也基于mnist\u deep.py源代码),试图恢复先前保存的训练模型,并将测试图像应用于该模型,以期获得相同的精度
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)
、丢失
、优化器
,以及准确性之类的东西……)我只是这样恢复了保存的模型:(一旦会话打开)
在会话开始时,我还删除了全局变量初始化,因为全局变量的值应该从经过训练的模型中恢复:
但是,为了能够应用该模型来识别给定测试图像的数字(cf函数识别数字图像(sess,x,y_conv,keep_prob,image)
),我仍然需要得到张量变量x,y_conv和keep_prob。因此,在从磁盘还原模型后,我添加了以下行:
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得到相同的结果
不幸的是,我在第一次调用通过名称()获取张量时遇到了一个异常:
另一个get\u tensor\u by\u name()
调用也会引发同样的异常
我做错了什么?为什么不可能这样得到这些张量
以下是我的完整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)
# 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)
这里是我的完整mnist\u deep\u restore\u trained\u 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 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)
# 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:
#创建模型
x=tf.placeholder(tf.float32,[None,784])
#定义损失和优化器
y=tf.占位符(tf.float32,[None,10])
。。。因此,它们在保存的图形中被命名为Placeholder
和Placeholder_1
,因此出现错误。将此代码更改为:
#创建模型
x=tf.placeholder(tf.float32,[None,784],name='x')
#定义损失和优化器
y=tf.placeholder(tf.float32,[None,10],name='y')
。。。同样,对于
keep_prob
和y_conv
(用于为+
操作命名)。顺便说一下,命名所有变量和键操作并使用。重新培训模型后,您的mnist\u deep\u restore\u trained\u model.py
应该可以工作。感谢您的帮助。现在很好用
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)
#禁用过梁警告以保持与教程的一致性。
#pylint:disable=无效名称
#pylint:disable=g-bad-import-order
从未来导入绝对导入
来自未来进口部
来自未来导入打印功能
导入argparse
导入系统
导入临时文件
随机输入
从tensorflow.examples.tutorials.mnist导入输入数据
导入tensorflow作为tf
标志=无
def deepnn(x):
“”“deepnn为数字分类的深网构建图形。
Args:
x:具有维度的输入张量(N_示例,784),其中784是
标准MNIST图像中的像素数。
返回:
元组(y,keep_prob)。y是形状张量(N_示例,10),具有值
等于将数字划分为10个类别之一的逻辑(
数字0-9)。keep_prob是表示以下概率的标量占位符:
辍学者
"""
#重塑以在卷积神经网络中使用。
#最后一个维度是“特征”-这里只有一个维度,因为图像是
#灰度——RGB图像为3,RGBA图像为4,等等。
使用tf.name_范围(“重塑”):
x_image=tf.重塑(x,[-1,28,28,1])
#第一个卷积层-将一个灰度图像映射到32个特征映射。
使用tf.name_作用域('conv1'):
W_conv1=权重_变量([5,5,1,32])
b_conv1=偏差_变量([32])
h_conv1=tf.nn.relu(conv2d(x_图像,W_conv1)+b_conv1)
#池层-向下采样2倍。
使用tf.name_作用域('pool1'):
h_池1=最大池2(h_池1)
#第二个卷积层——将32个特征映射映射到64个。
使用tf.name_作用域('conv2'):
W_conv2=权重_变量([5,5,32,64])
b_conv2=偏差_变量([64])
h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)
#第二池层。
使用tf.name_作用域('pool2'):
h_池2=最大池2×2(h_池2)
#完全连接的第1层--经过2轮下采样,我们的28x28图像
#减少到7x7x64功能映射--将其映射到1024个功能。
使用tf.name_范围('fc1'):
W_fc1=权重_变量([7*7*641024])
b_fc1=偏差_变量([1024])
h_pool2_flat=tf.重塑(h_pool2,[-1,7*7*64])
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_
# 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)