近一个假期，入坑深度学习，先从能看得着的验证码识别入门。从B站看了几天的黑马程序员的“3天带你玩转python深度学习后“，一是将教程中提到的代码一一码出来；二是针对不同的tensorflow版本，结合网络上其它文章，重新利用tensorflow2.x的keras实现同样的功能。两遍代码写完后，深感深度学习的恐怖。
一、Anaconda安装。
1.为了一些不必要的麻烦，还是先安装anaconda。下载地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/
（这个版本支持python3.8.3)
2.下载完成后，啥也不说了，直接安装即可，能选择的一般都选择上，特别是一些环境变量的设置等。
3.安装完成后，在“Anaconda prompt"里，使用如下命令安装tensorflow
pip install tensorflow-cpu==2.2.0 -i https://pypi.doubanio.com/simple/
(注意：本机没有nvidia显卡，所以只能使用cpu版本；另，至于网上说防止出现“avx2“啥的警告，到Github上下载avx版本，去了之后会发现……木法下载，还是用这个版本吧）
4.如上，安装tensorflow完成，你可以测试一下下了。
import tensorflow as tf print(tf.__version__)
如果提示“Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2”，禁用警告吧。方法如下：
import os os.environ['TF_CPP_MIN_LOG_LEVEL']=2
二、因为我们的代码需要分别在tensorflow的不同版本上跑，而tensorflow1.x和2.x几乎是断代的，所以需要在anaconda中再配置一个低版本的环境。
1.先设置一下conda的国内源，找到用户文件夹下的.condarc文件，编辑如下：
channels: - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ show_channel_urls: true ssl_verify: true
2.进入anaconda prompt，在conda中新建一个python3.5的环境，并进入这个环境，然后安装tensorflow1.8,pandas。
conda create -n python3.5 python=3.5
conda activate python3.5
pip install tensorflow==1.8 -i https://pypi.doubanio.com/simple/
pip install pandas -i https://pypi.doubanio.com/simple/
三、验证码识别的代码如下：
1.tensorflow1.8版本：
import tensorflow as tf import glob, os,io,sys import pandas as pd import numpy as np os.environ["TF_CPP_MIN_LOG_LEVEL"]='2' def read_pic(): file_list=glob.glob("./code_imgs/*.png") file_queue=tf.train.string_input_producer(file_list) reader=tf.WholeFileReader() filename,image=reader.read(file_queue) decoded=tf.image.decode_png(image,channels=3) decoded.set_shape([28,96,3]) image_cast=tf.cast(decoded,tf.float32) filename_batch,image_batch=tf.train.batch([filename,image_cast],batch_size=40,num_threads=2,capacity=40) return filename_batch,image_batch def parse_csv(): csv_data=pd.read_csv('labels.csv',names=['file_num','chars'],index_col='file_num') labels=[] for label in csv_data["chars"]: letter=[] for word in label: letter.append(ord(word)-ord('a')) labels.append(letter) csv_data['labels']=labels return csv_data def filename2label(filenames,csv_data): labels=[] for filename in filenames: file_num="".join(filter(str.isdigit,str(filename))) target=csv_data.loc[int(file_num),"labels"] labels.append(target) return np.array(labels) def create_weights(shape): return tf.Variable(initial_value=tf.random_normal(shape=shape,stddev=0.01)) def create_model(x): with tf.variable_scope('conv1'): conv1_weights=create_weights(shape=[5,5,3,32]) conv1_bias=create_weights(shape=[32]) conv1_x=tf.nn.conv2d(input=x,filter=conv1_weights,strides=[1,1,1,1],padding='SAME')+conv1_bias relu1_x=tf.nn.relu(conv1_x) pool1_x=tf.nn.max_pool(value=relu1_x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') with tf.variable_scope('conv2'): conv2_weights=create_weights(shape=[5,5,32,64]) conv2_bias=create_weights(shape=[64]) conv2_x=tf.nn.conv2d(input=pool1_x,filter=conv2_weights,strides=[1,1,1,1],padding='SAME')+conv2_bias relu2_x=tf.nn.relu(conv2_x) pool2_x=tf.nn.max_pool(value=relu2_x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') with tf.variable_scope('full_connection'): x_fc=tf.reshape(pool2_x,shape=[-1,7*24*64]) weights_fc=create_weights(shape=[7*24*64,104]) bias_fc=create_weights(shape=[104]) y_predict=tf.matmul(x_fc,weights_fc)+bias_fc return y_predict def list2text(textlist): tm=[] for i in textlist: tm.append(chr(97+i)) return "".join(tm) def train(): filename,image=read_pic() csv_data=parse_csv() x=tf.placeholder(tf.float32,shape=[None,28,96,3]) y_true=tf.placeholder(tf.float32,shape=[None,104]) y_predict=create_model(x) loss_list=tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true,logits=y_predict) loss=tf.reduce_mean(loss_list) optimizer=tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)#优化损失 equal_list=tf.reduce_all(tf.equal(tf.argmax(tf.reshape(y_predict,shape=[-1,4,26]),axis=2), tf.argmax(tf.reshape(y_true,shape=[-1,4,26]),axis=2)),axis=1) accurary=tf.reduce_mean(tf.cast(equal_list,tf.float32)) init=tf.global_variables_initializer() saver=tf.train.Saver() with tf.Session() as sess: sess.run(init) coord=tf.train.Coordinator() threads=tf.train.start_queue_runners(sess=sess,coord=coord) try: for i in range(10000): filename_val,image_val=sess.run([filename,image]) labels=filename2label(filename_val,csv_data) labels_value=tf.reshape(tf.one_hot(labels,depth=26),[-1,104]).eval() _,error,accurary_value=sess.run([optimizer,loss,accurary],feed_dict={x:image_val,y_true:labels_value}) print("The %d Train Result---loss:%f,accurary:%f" % (i+1,error,accurary_value)) if accurary_value>0.99: saver.save(sess,'model/crack_captcha.model99',global_step=i) break except tf.errors.OutOfRangeError: print("done ,now let's kill all threads") finally: coord.request_stop() print("all threads ask stop") coord.join(threads) print("all thread stopped") def crackcaptcha(): truetext=[] with open('a.txt','r') as f: for filename in f.readlines(): truetext.append(filename.strip('\r\n')) dis='False' goodnum=0 x=tf.placeholder(tf.float32,shape=[None,28,96,3]) y_true=tf.placeholder(tf.float32,shape=[None,104]) keep_prob = tf.placeholder(tf.float32) y_predict=create_model(x) saver=tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) checkpoint=tf.train.get_checkpoint_state('model') if checkpoint and checkpoint.model_checkpoint_path: saver.restore(sess,checkpoint.model_checkpoint_path) print("successfully loaded:",checkpoint.model_checkpoint_path) else: print("Could not found model files") for i in range(1,201): image=tf.read_file('crackimgs/'+str(i)+'.png') decoded=tf.image.decode_png(image,channels=3) decoded.set_shape([28,96,3]) decoded_val=sess.run(decoded) image=np.array(decoded_val) predict=tf.argmax(tf.reshape(y_predict,[-1,4,26]),2) outtext=sess.run(predict,feed_dict={x:[image],keep_prob:1}) cracktext=list2text(outtext[0].tolist()) if cracktext==truetext[i-1]: goodnum+=1 dis='True' else: dis='False' print('The {} Image Content is:{},Your Crack Word is :{},Result:{}'.format(i,truetext[i-1],cracktext,dis)) print('The End accurary is:{}%'.format(goodnum/200*100)) if __name__=='__main__': train() crackcaptcha()