最近刚接触tensorflow,同样和广大网友一样采用MINIST数据来做手写识别,内容以注释的形式在代码里了
模型训练和保存
1.首先下载MINIST数据库(下载地址) ,四个文件下载后放到和你的python文件同一个目录下,不用解压,然后输入,其中e2.jpg在文末可下载
#coding=utf-8 # 载入MINIST数据需要的库 from tensorflow.examples.tutorials.mnist import input_data # 保存模型需要的库 from tensorflow.python.framework.graph_util import convert_variables_to_constants from tensorflow.python.framework import graph_util # 导入其他库 import tensorflow as tf import cv2 import numpy as np #获取MINIST数据 mnist = input_data.read_data_sets(".",one_hot = True) # 创建会话 sess = tf.InteractiveSession() #占位符 x = tf.placeholder("float", shape=[None, 784], name="Mul") y_ = tf.placeholder("float",shape=[None, 10], name="y_") #变量 W = tf.Variable(tf.zeros([784,10]),name='x') b = tf.Variable(tf.zeros([10]),'y_') #权重 def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) #偏差 def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) #卷积 def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') #最大池化 def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #相关变量的创建 W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1,28,28,1]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) #激活函数 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]) b_fc1 = bias_variable([1024]) W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) 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("float",name='rob') h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) #用于训练用的softmax函数 y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2,name='res') #用于训练作完后,作测试用的softmax函数 y_conv2=tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2,name="final_result") #交叉熵的计算,返回包含了损失值的Tensor。 cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) #优化器,负责最小化交叉熵 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) #计算准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) #初始化所以变量 sess.run(tf.global_variables_initializer()) # 保存输入输出,可以为之后用 tf.add_to_collection('res', y_conv) tf.add_to_collection('output', y_conv2) tf.add_to_collection('x', x) #训练开始 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) #run()可以看做输入相关值给到函数中的占位符,然后计算的出结果,这里将batch[0],给xbatch[1]给y_ train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) #将当前图设置为默认图 graph_def = tf.get_default_graph().as_graph_def() #将上面的变量转化成常量,保存模型为pb模型时需要,注意这里的final_result和前面的y_con2是同名,只有这样才会保存它,否则会报错, # 如果需要保存其他tensor只需要让tensor的名字和这里保持一直即可 output_graph_def = tf.graph_util.convert_variables_to_constants(sess, graph_def, ['final_result']) #保存前面训练后的模型为pb文件 with tf.gfile.GFile("grf.pb", 'wb') as f: f.write(output_graph_def.SerializeToString()) #用saver 保存模型 saver = tf.train.Saver() saver.save(sess, "model_data/model") #导入图片,同时灰度化 im = cv2.imread('pic/e2.jpg',cv2.IMREAD_GRAYSCALE) #反转图像,因为e2.jpg为白底黑字 im =reversePic(im) cv2.namedWindow("camera", cv2.WINDOW_NORMAL); cv2.imshow('camera',im) cv2.waitKey(0) #调整大小 im = cv2.resize(im,(28,28),interpolation=cv2.INTER_CUBIC) x_img = np.reshape(im , [-1 , 784]) #输出图像矩阵 # print x_img #用上面导入的图片对模型进行测试 output = sess.run(y_conv2 , feed_dict={x:x_img }) # print 'the y_con : ', '\n',output print 'the predict is : ', np.argmax(output) print "test accracy %g"%accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
输出:
没有CUDA加速,训练的会比较慢,但都可以训练,只是速度区别
1)其中用Saver保存模型的代码:
saver = tf.train.Saver() saver.save(sess, "model_data/model")
最终会产生model_data文件夹,其中包含了:
2)保存模型为pb格式的代码:
#将当前图设置为默认图 graph_def = tf.get_default_graph().as_graph_def() #将上面的变量转化成常量,保存模型为pb模型时需要,注意这里的final_result和前面的y_con2是同名,只有这样才会保存它,否则会报错, # 如果需要保存其他tensor只需要让tensor的名字和这里保持一直即可 output_graph_def = tf.graph_util.convert_variables_to_constants(sess, graph_def, ['final_result']) #保存前面训练后的模型为pb文件 with tf.gfile.GFile("grf.pb", 'wb') as f: f.write(output_graph_def.SerializeToString())
最终在当前目录生成grf.pb文件
模型的恢复:
1.用Saver保存的模型的恢复:
# -*- coding:utf-8 -*- import cv2 import tensorflow as tf import numpy as np from sys import path #用于将自定义输入图片反转 def reversePic(src): # 图像反转 for i in range(src.shape[0]): for j in range(src.shape[1]): src[i,j] = 255 - src[i,j] return src def main(): sess = tf.InteractiveSession() #模型恢复 saver=tf.train.import_meta_graph('model_data/model.meta') saver.restore(sess, 'model_data/model') graph = tf.get_default_graph() # 获取输入tensor,,获取输出tensor input_x = sess.graph.get_tensor_by_name("Mul:0") y_conv2 = sess.graph.get_tensor_by_name("final_result:0") # 也可以上面注释,通过下面获取输出输入tensor, # y_conv2 = tf.get_collection('output')[0] # # x= tf.get_collection('x')[0] # input_x = graph.get_operation_by_name('Mul').outputs[0] # keep_prob = graph.get_operation_by_name('rob').outputs[0] path="pic/e2.jpg" im = cv2.imread(path,cv2.IMREAD_GRAYSCALE) #反转图像,因为e2.jpg为白底黑字 im =reversePic(im) cv2.namedWindow("camera", cv2.WINDOW_NORMAL); cv2.imshow('camera',im) cv2.waitKey(0) # im=cv2.threshold(im, , 255, cv2.THRESH_BINARY_INV)[1]; im = cv2.resize(im,(28,28),interpolation=cv2.INTER_CUBIC) # im=cv2.threshold(im,200,255,cv2.THRESH_TRUNC)[1] # im=cv2.threshold(im,60,255,cv2.THRESH_TOZERO)[1] #数据从0~255转为-0.5~0.5 # img_gray = (im - (255 / 2.0)) / 255 x_img = np.reshape(im , [-1 , 784]) output = sess.run(y_conv2 , feed_dict={input_x:x_img}) print 'the predict is %d' % (np.argmax(output)) #关闭会话 sess.close() if __name__ == '__main__': main()
2.pb模型的恢复
#coding=utf-8 from __future__ import absolute_import, unicode_literals from tensorflow.examples.tutorials.mnist import input_data from tensorflow.python.framework.graph_util import convert_variables_to_constants from tensorflow.python.framework import graph_util import cv2 import numpy as np mnist = input_data.read_data_sets(".",one_hot = True) import tensorflow as tf #用于将自定义输入图片反转 def reversePic(src): # 图像反转 for i in range(src.shape[0]): for j in range(src.shape[1]): src[i,j] = 255 - src[i,j] return src with tf.Session() as persisted_sess: print("load graph") with tf.gfile.FastGFile("grf.pb",'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) persisted_sess.graph.as_default() tf.import_graph_def(graph_def, name='') # print("map variables") with tf.Session() as sess: # tf.initialize_all_variables().run() input_x = sess.graph.get_tensor_by_name("Mul:0") y_conv_2 = sess.graph.get_tensor_by_name("final_result:0") path="pic/e2.jpg" im = cv2.imread(path,cv2.IMREAD_GRAYSCALE) #反转图像,因为e2.jpg为白底黑字 im =reversePic(im) cv2.namedWindow("camera", cv2.WINDOW_NORMAL); cv2.imshow('camera',im) cv2.waitKey(0) # im=cv2.threshold(im, , 255, cv2.THRESH_BINARY_INV)[1]; im = cv2.resize(im,(28,28),interpolation=cv2.INTER_CUBIC) # im =reversePic(im) # im=cv2.threshold(im,200,255,cv2.THRESH_TRUNC)[1] # im=cv2.threshold(im,60,255,cv2.THRESH_TOZERO)[1] # img_gray = (im - (255 / 2.0)) / 255 x_img = np.reshape(im , [-1 , 784]) output = sess.run(y_conv_2 , feed_dict={input_x:x_img}) print 'the predict is %d' % (np.argmax(output)) #关闭会话 sess.close()
其中e2.jpg:
输出结果都是:
注意:
用MINIST训练出来的模型。主要用来识别手写数字的,而且对输入的图片要求是近似黑底白字的,所以如果图片预处理不合适会导致识别率不高。
如果直接用官方的图片输 入,则识别完全没问题
附官方图片和e2.jpg的下载地址