tensorflow Lenet5手写字体识别模型的保存与加载

本文主要是介绍tensorflow Lenet5手写字体识别模型的保存与加载，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

网上基本上都是tensorflow中给的手写字体的模型的训练过程，没有根据模型识别单个图片的相关内容，废话不多说，接下来给出模型的训练，保存，并根据训练好的模型的识别某个图片的数字。

1.模型的训练

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
sess = tf.InteractiveSession()def weight_variable(shape,namew='w'):initial = tf.truncated_normal(shape, stddev=0.1)return tf.Variable(initial,name=namew)def bias_variable(shape,nameb='b'):initial = tf.constant(0.1, shape=shape)return tf.Variable(initial,name=nameb)def conv2d(x, W,namec='c'):return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME',name=namec)def max_pool_2x2(x,namep='p'):return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME',name=namep)x = tf.placeholder(tf.float32, [None, 784],name='xinput')
y_ = tf.placeholder(tf.float32, [None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])# Conv1 Layer
W_conv1 = weight_variable([5, 5, 1, 32],'w1')
b_conv1 = bias_variable([32],'b1')
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1,'c1')
h_pool1 = max_pool_2x2(h_conv1,'p1')# Conv2 Layer
W_conv2 = weight_variable([5, 5, 32, 64],'w2')
b_conv2 = bias_variable([64],'b2')
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)W_fc1 = weight_variable([7 * 7 * 64, 1024],'wf1')
b_fc1 = bias_variable([1024],'bf1')
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)keep_prob = tf.placeholder(tf.float32,name="prob")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)W_fc2 = weight_variable([1024, 10],'wfull2')
b_fc2 = bias_variable([10],'bf2')
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
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, tf.float32))tf.global_variables_initializer().run()saver = tf.train.Saver();
start = time.clock()
for i in range(500):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})print("test accuracy %g" % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
end = time.clock()
print(end - start)
saver.save(sess, "Model/model.ckpt")

关键点：

saver = tf.train.Saver(); saver.save(sess, "Model/model.ckpt")
要特别注意给变量添加名字 如
w2 = tf.placeholder("float", name="w2")
b1= tf.Variable(2.0,name="bias")
名字是加载模型时主要依据
2.加载模型
import tensorflow as tf
from skimage import io
import numpy as np
import cv2def conv2d(x, W):return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')sess = tf.Session()
saver = tf.train.import_meta_graph("Model/model.ckpt.meta")
saver.restore(sess,tf.train.latest_checkpoint('./Model'))im = cv2.imread('1.jpg', cv2.IMREAD_GRAYSCALE).astype(np.float32)# im = cv2.resize(im, (28, 28), interpolation=cv2.INTER_CUBIC)
# 图片预处理
# img_gray = cv2.cvtColor(im , cv2.COLOR_BGR2GRAY).astype(np.float32)
# 数据从0~255转为-0.5~0.5
img_gray = (im - (255 / 2.0)) / 255
# img_gray = im / 255x_image = tf.reshape(im, [-1, 28, 28, 1])
# x_img = np.reshape(img_gray, [-1, 784])W_conv1 = sess.run('w1:0')
b_conv1 = sess.run('b1:0')
cov1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
p1=max_pool_2x2(cov1)W_conv2 = sess.run('w2:0')
b_conv2 = sess.run('b2:0')
cov2=tf.nn.relu(conv2d(p1,W_conv2)+b_conv2)
p2=max_pool_2x2(cov2)Wf1 = sess.run('wf1:0')
bf1 = sess.run('bf1:0')
h_pool2_flat = tf.reshape(p2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, Wf1) + bf1)# graph = tf.get_default_graph()
# keep_prob = graph.get_tensor_by_name("prob:0")
# init = tf.global_variables_initializer() #加载模型绝对不能添加变量初始化  这条语句之后的变量初始化
# sess.run(init)
# keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob=1.0)W_fc2 = sess.run('wfull2:0')
b_fc2 = sess.run('bf2:0')y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
# print(b_fc2)
result=sess.run(y_conv)
print(sess.run(y_conv))
print("the result of predict is:",np.argmax(result))

关键点1：模型加载语句
saver = tf.train.import_meta_graph("Model/model.ckpt.meta")
saver.restore(sess,tf.train.latest_checkpoint('./Model'))
注意上面的两个路径一个加了点，一个没有加点
关键点2：获得变量
Wf1 = sess.run('wf1:0')
直接加载原模型中的变量
关键点3：绝对不能变量初始化，否则模型中的变量会被重新赋值
init = tf.global_variables_initializer() #加载模型绝对不能添加变量初始化  这条语句之后的变量重新初始化
sess.run(init)
上面的语句不能有

最后附一张结果截图：