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訓(xùn)練模型

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sat Mar 16 22:26:43 2019@author: lg """#coding=utf-8 # 載入MINIST數(shù)據(jù)需要的庫(kù) from tensorflow.examples.tutorials.mnist import input_data # 保存模型需要的庫(kù) from tensorflow.python.framework.graph_util import convert_variables_to_constants from tensorflow.python.framework import graph_util # 導(dǎo)入其他庫(kù) import tensorflow as tf import cv2 import numpy as np #獲取MINIST數(shù)據(jù) mnist = input_data.read_data_sets(".",one_hot = True) # 創(chuàng)建會(huì)話(huà) sess = tf.InteractiveSession()#占位符 x = tf.placeholder("float", shape=[None, 784], name="Mul") y_ = tf.placeholder("float",shape=[None, 10], name="y_") #變量 W = tf.Variable(tf.zeros([784,10]),name='x') b = tf.Variable(tf.zeros([10]),'y_')#權(quán)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) #卷積 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') #相關(guān)變量的創(chuàng)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]) #激活函數(shù) 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)#用于訓(xùn)練用的softmax函數(shù) y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2,name='res') #用于訓(xùn)練作完后，作測(cè)試用的softmax函數(shù) y_conv2=tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2,name="final_result")#交叉熵的計(jì)算，返回包含了損失值的Tensor。cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) #優(yōu)化器，負(fù)責(zé)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)) #計(jì)算準(zhǔn)確率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) #初始化所以變量 sess.run(tf.global_variables_initializer())# 保存輸入輸出，可以為之后用 tf.add_to_collection('res', y_conv) tf.add_to_collection('output', y_conv2) tf.add_to_collection('x', x)#訓(xùn)練開(kāi)始 for i in range(1000):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()可以看做輸入相關(guān)值給到函數(shù)中的占位符，然后計(jì)算的出結(jié)果，這里將batch[0]，給xbatch[1]給y_train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})#將當(dāng)前圖設(shè)置為默認(rèn)圖 graph_def = tf.get_default_graph().as_graph_def() #將上面的變量轉(zhuǎn)化成常量，保存模型為pb模型時(shí)需要,注意這里的final_result和前面的y_con2是同名，只有這樣才會(huì)保存它，否則會(huì)報(bào)錯(cuò)， # 如果需要保存其他tensor只需要讓tensor的名字和這里保持一直即可 output_graph_def = tf.graph_util.convert_variables_to_constants(sess, graph_def, ['final_result']) #保存前面訓(xùn)練后的模型為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") ##導(dǎo)入圖片，同時(shí)灰度化 #im = cv2.imread('pic/e2.jpg',cv2.IMREAD_GRAYSCALE) ##反轉(zhuǎn)圖像，因?yàn)閑2.jpg為白底黑字 #im =reversePic(im) #cv2.namedWindow("camera", cv2.WINDOW_NORMAL); #cv2.imshow('camera',im) #cv2.waitKey(0) # ##調(diào)整大小 #im = cv2.resize(im,(28,28),interpolation=cv2.INTER_CUBIC) #x_img = np.reshape(im , [-1 , 784]) # # ##輸出圖像矩陣 ## print x_img # ##用上面導(dǎo)入的圖片對(duì)模型進(jìn)行測(cè)試 #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}))

加載模型第一種

# -*- coding:utf-8 -*- import cv2 import tensorflow as tf import numpy as np from sys import path #用于將自定義輸入圖片反轉(zhuǎn) def reversePic(src):# 圖像反轉(zhuǎn) 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() #模型恢復(fù)saver=tf.train.import_meta_graph('model_data/model.meta')saver.restore(sess, 'model_data/model')graph = tf.get_default_graph()# 獲取輸入tensor,,獲取輸出tensorinput_x = sess.graph.get_tensor_by_name("Mul:0")y_conv2 = sess.graph.get_tensor_by_name("final_result:0")# 也可以上面注釋,通過(guò)下面獲取輸出輸入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]path1="pic/e2.jpg" im = cv2.imread(path1,cv2.IMREAD_GRAYSCALE)#反轉(zhuǎn)圖像，因?yàn)閑2.jpg為白底黑字 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]#數(shù)據(jù)從0~255轉(zhuǎn)為-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)) )#關(guān)閉會(huì)話(huà) sess.close() if __name__ == '__main__': main()

加載模型第二種

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Mar 17 11:15:53 2019@author: lg """#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#用于將自定義輸入圖片反轉(zhuǎn) def reversePic(src):# 圖像反轉(zhuǎn) 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) #反轉(zhuǎn)圖像，因?yàn)閑2.jpg為白底黑字 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)) )#關(guān)閉會(huì)話(huà) sess.close()

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