TensorFlow入门：MNIST预测[restore问题]
变量的恢复可按照两种方式导入：
saver=tf.train.Saver() saver.restore(sess,'model.ckpt')
或者：
saver=tf.train.import_meta_graph(r'D:\tmp\tensorflow\mnist\model.ckpt.meta') saver.restore(sess,'model.ckpt')
两种方法的效果应该一致，但是实际结果不一样：
使用前者时预测结果是一致的；使用后者时，每次运行结果都不一致。无论是否重启spyde，现象都一样。
在使用前者时，必须在运行前重启spyde，否则会报错，为什么？Out_1等参数会随运行次数增加
INFO:tensorflow:Restoring parameters from D:/tmp/tensorflow/mnist/model.ckpt
Traceback (most recent call last):
File "<ipython-input-2-61410824b24c>", line 1, in <module>
runfile('D:/wangjc/pythonTest/TensorFlow/TestMNIST_Predict.py', wdir='D:/wangjc/pythonTest/TensorFlow')
......
File "D:\software\anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1052, in _do_call
raise type(e)(node_def, op, message)
NotFoundError: Key out_1/bias/bias not found in checkpoint
[[Node: save_1/RestoreV2_14 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save_1/Const_0, save_1/RestoreV2_14/tensor_names, save_1/RestoreV2_14/shape_and_slices)]]
Caused by op 'save_1/RestoreV2_14', defined at:
File "D:\software\anaconda\envs\tensorflow\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 241, in <module>
main()
......File "D:\software\anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 1228, in __init__
self._traceback = _extract_stack()
NotFoundError (see above for traceback): Key out_1/bias/bias not found in checkpoint
[[Node: save_1/RestoreV2_14 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save_1/Const_0, save_1/RestoreV2_14/tensor_names, save_1/RestoreV2_14/shape_and_slices)]]
NotFoundError: Key out_2/weight/weight not found in checkpoint
[[Node: save_2/RestoreV2_23 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save_2/Const_0, save_2/RestoreV2_23/tensor_names, save_2/RestoreV2_23/shape_and_slices)]]
Caused by op 'save_2/RestoreV2_23', defined at:
以上需要重启spyder的原因为saver恢复一次之后不能再次恢复，否则报错。
导致saver=tf.train.Saver()与saver=tf.train.import_meta_graph(r'D:\tmp\tensorflow\mnist\model.ckpt.meta')结果不同的原因是，后者在使用中可直接加载模型的参数，操作数等。
tf.get_default_graph()获取图
.get_tensor_by_name()获取张量
.get_operation_by_name()获取操作
注意对各部分命名。
使用下面方法的效果与直接读ckpt文件一致
saver = tf.train.Saver() ckpt=tf.train.get_checkpoint_state(r'D:\tmp\tensorflow\mnist') saver.restore(sess, ckpt.model_checkpoint_path)
可使用tf.get_collection('name')来读取恢复的变量
注意定义变量时最好标注标量名称，否则可能出现预测时加载参数不正确，定义方法为：
def weight_variable(shape):
#use normal distribution numbers with stddev 0.1 to initial the weight
initial=tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial,name='weight')
-------------------------------------------------------------------------------------------------------------------------------
训练并保存模型 代码
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 11 10:16:34 2017
multy layers softmax regression
@author: Wangjc
"""
import tensorflow as tf
import os
import tensorflow.examples.tutorials.mnist.input_data as input_data
#need to show the full address, or error occus.
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
#use read_data_sets to download and load the mnist data set. if has the data, then load.
#need a long time about 5 minutes
sess = tf.InteractiveSession()
#link the back-end of C++ to compute.
#in norm cases, we should create the map and then run in the sussion.
#now, use a more convenient class named InteractiveSession which could insert compute map when running map.
x=tf.placeholder("float",shape=[None,784])
y_=tf.placeholder("float",shape=[None,10])
def weight_variable(shape):
#use normal distribution numbers with stddev 0.1 to initial the weight
initial=tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial,name='weight')
def bias_variable(shape):
#use constant value of 0.1 to initial the bias
initial=tf.constant(0.1, shape=shape)
return tf.Variable(initial,name='bias')
def conv2d(x,W):
#convolution by filter of W,with step size of 1, 0 padding size
#x should have the dimension of [batch,height,width,channels]
#other dimension of strides or ksize is the same with x
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
def max_pool_2x2(x):
#pool by windows of ksize,with step size of 2, 0 padding size
return tf.nn.max_pool(x,ksize=[1,2,2,1],
strides=[1,2,2,1],padding='SAME')
#------------------------------------------------
x_image = tf.reshape(x, [-1,28,28,1])
#to use conv1, need to convert x to 4D, in form of [batch,height,width,channels]
# -1 means default
with tf.name_scope('conv1'):
#use 'with' and name_scope to define a name space which will show in tensorboard as a ragion
with tf.name_scope('weight'):
W_conv1=weight_variable([5,5,1,32])
tf.summary.histogram('conv1'+'/weight',W_conv1)
#summary the variation ('name', value)
with tf.name_scope('bias'):
b_conv1=bias_variable([32])
tf.summary.histogram('conv1'+'/bias',b_conv1)
#build the first conv layer:
#get 32 features from every 5*5 patch, so the shape is [5,5,1(channel),32]
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
with tf.name_scope('pool1'):
h_pool1 = max_pool_2x2(h_conv1)
#--------------------------------------------
with tf.name_scope('conv2'):
with tf.name_scope('weight'):
W_conv2=weight_variable([5,5,32,64])
tf.summary.histogram('weight',W_conv2)
with tf.name_scope('bias'):
b_conv2=bias_variable([64])
tf.summary.histogram('bias',b_conv2)
#build the 2nd conv layer:
#get 64 features from every 5*5 patch, so the shape is [5,5,32(channel),64]
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
with tf.name_scope('pool2'):
h_pool2 = max_pool_2x2(h_conv2)
#----------------------------------------
#image size reduce to 7*7 by pooling
#we add a full connect layer contains 1027 nuere
#need to flat pool tensor for caculate
with tf.name_scope('fc1'):
with tf.name_scope('weight'):
W_fc1 = weight_variable([7*7*64, 1024])
tf.summary.histogram('weight',W_fc1)
with tf.name_scope('bias'):
b_fc1 = bias_variable([1024])
tf.summary.histogram('bias',b_fc1)
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)
#------------------------------------
#output layer
with tf.name_scope('out'):
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)
#to decrease overfit, we add dropout before output layer.
#use placeholder to represent the porbability of a neure's output value unchange
with tf.name_scope('weight'):
W_fc2 = weight_variable([1024, 10])
tf.summary.histogram('weight',W_fc2)
with tf.name_scope('bias'):
b_fc2 = bias_variable([10])
tf.summary.histogram('bias',b_fc2)
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
#---------------------------------
#train and evaluate the module
#use a ADAM
cross_entropy=-tf.reduce_sum(y_*tf.log(y_conv))
tf.summary.scalar('cross_entropy',cross_entropy)
##summary the constant ('name', value)
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
#sess = tf.Session()
merged=tf.summary.merge_all()
#merge all the summary nodes
writer=tf.summary.FileWriter('D:/tmp/tensorflow/mnist/',sess.graph)
# assign the event file write directory
saver=tf.train.Saver()
#saver for variation.Dafault to save all.
checkpoint_file = os.path.join('D:/tmp/tensorflow/mnist/', 'model.ckpt')
#save directroy for variation
sess.run(tf.global_variables_initializer())
for i in range(100):
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})
# saver.save(sess,checkpoint_file)
# saver.save(sess,checkpoint_file,global_step=i)
#save variation
print("step %d, training accuracy %g"%(i, train_accuracy))
result=sess.run(merged,feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
#the merged summary need to be run
writer.add_summary(result,i)
#add the result to summary
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
saver.save(sess,checkpoint_file)
读取图片，恢复参数并预测 代码
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 11 10:16:34 2017
multy layers softmax regression
@author: Wangjc
"""
import tensorflow as tf
import os
import cv2
import matplotlib.pyplot as plt
#need to show the full address, or error occus.
imgs0=cv2.imread(r'D:\tmp\tensorflow\imgs\_1.png',0)
plt.imshow(imgs0)
plt.show()
imgs=imgs0/255
#imgs=(255-imgs0)/255
imgs.shape=(1,784)
sess = tf.InteractiveSession()
#link the back-end of C++ to compute.
#in norm cases, we should create the map and then run in the sussion.
#now, use a more convenient class named InteractiveSession which could insert compute map when running map.
x=tf.placeholder("float",shape=[None,784])
def weight_variable(shape):
#use normal distribution numbers with stddev 0.1 to initial the weight
initial=tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial,name='weight')
def bias_variable(shape):
#use constant value of 0.1 to initial the bias
initial=tf.constant(0.1, shape=shape)
return tf.Variable(initial,name='bias')
def conv2d(x,W):
#convolution by filter of W,with step size of 1, 0 padding size
#x should have the dimension of [batch,height,width,channels]
#other dimension of strides or ksize is the same with x
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
def max_pool_2x2(x):
#pool by windows of ksize,with step size of 2, 0 padding size
return tf.nn.max_pool(x,ksize=[1,2,2,1],
strides=[1,2,2,1],padding='SAME')
#------------------------------------------------
x_image = tf.reshape(x, [-1,28,28,1])
#to use conv1, need to convert x to 4D, in form of [batch,height,width,channels]
# -1 means default
with tf.name_scope('conv1'):
#use 'with' and name_scope to define a name space which will show in tensorboard as a ragion
with tf.name_scope('weight'):
W_conv1=weight_variable([5,5,1,32])
# tf.summary.histogram('conv1'+'/weight',W_conv1)
#summary the variation ('name', value)
with tf.name_scope('bias'):
b_conv1=bias_variable([32])
# tf.summary.histogram('conv1'+'/bias',b_conv1)
#build the first conv layer:
#get 32 features from every 5*5 patch, so the shape is [5,5,1(channel),32]
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
with tf.name_scope('pool1'):
h_pool1 = max_pool_2x2(h_conv1)
#--------------------------------------------
with tf.name_scope('conv2'):
with tf.name_scope('weight'):
W_conv2=weight_variable([5,5,32,64])
# tf.summary.histogram('weight',W_conv2)
with tf.name_scope('bias'):
b_conv2=bias_variable([64])
# tf.summary.histogram('bias',b_conv2)
#build the 2nd conv layer:
#get 64 features from every 5*5 patch, so the shape is [5,5,32(channel),64]
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
with tf.name_scope('pool2'):
h_pool2 = max_pool_2x2(h_conv2)
#----------------------------------------
#image size reduce to 7*7 by pooling
#we add a full connect layer contains 1027 nuere
#need to flat pool tensor for caculate
with tf.name_scope('fc1'):
with tf.name_scope('weight'):
W_fc1 = weight_variable([7*7*64, 1024])
# tf.summary.histogram('weight',W_fc1)
with tf.name_scope('bias'):
b_fc1 = bias_variable([1024])
# tf.summary.histogram('bias',b_fc1)
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)
#------------------------------------
#output layer
with tf.name_scope('out'):
keep_prob = tf.placeholder("float")
# h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)
#to decrease overfit, we add dropout before output layer.
#use placeholder to represent the porbability of a neure's output value unchange
with tf.name_scope('weight'):
W_fc2 = weight_variable([1024, 10])
# tf.summary.histogram('weight',W_fc2)
with tf.name_scope('bias'):
b_fc2 = bias_variable([10])
# tf.summary.histogram('bias',b_fc2)
y_conv = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)
#---------------------------------
#saver=tf.train.import_meta_graph(r'D:\tmp\tensorflow\mnist\model.ckpt.meta')
saver=tf.train.Saver()
#saver for variation.Dafault to save all.
checkpoint_file = os.path.join('D:/tmp/tensorflow/mnist/', 'model.ckpt')
#save directroy for variation
sess.run(tf.global_variables_initializer())
saver.restore(sess,checkpoint_file)
#saver.recover_last_checkpoints(checkpoint_file)
#prediction=tf.argmax(y_conv,1)
#result=prediction.eval(feed_dict={x: imgs})
result=sess.run(tf.argmax(y_conv,1),feed_dict={x: imgs,keep_prob: 0.5})
#result=prediction.eval(feed_dict={x: imgs,keep_prob: 0.5})
print('recognize result')
print(result[0])
