C:\Users\86186>calc C:\Users\86186>taskkill /f /im calc.exe 错误: 没有找到进程 "calc.exe"。

C:\Users\86186>calc C:\Users\86186>taskkill /f /im calc.exe 错误: 没有找到进程 "calc.exe"。 我的电脑已经成功打开了calc，并且任务管理器也有calc，但是就是taskkill找不到路径

关于matlab函数定义的问题，定义了但是提示未定义 求大神解答

 以下是关于函数的定义 function p = init_phi(im,type) im = dimensionz(im); %m = im(150:250,150:250); [dim1, dim2] = size(im); p = zeros(dim1+2,dim2+2); switch lower (type) case 'circle' for i = 1:dim1+2 for j = 1:dim2+2 p(i,j) = (sqrt((i/dim1-0.5)^2 + (j/dim2-0.5)^2) - 0.2) * 30; end end case 'grid' for i = 1:dim1+1 for j = 1:dim2+1 p(i,j) = sin(i*pi/5) + sin(j*pi/5); end end case 'circle 2' for i = 1:dim1+2 for j = 1:dim2+2 p(i,j) = (sqrt(((i+2)/dim1-0.3)^2 + (j/dim2-0.9)^2) - 0.2) * 30; end end case 'square' %p = zeros(dim1+2,dim2+2); p(floor((dim1+2)/3:(dim1+2)*2/3),floor((dim2+2)/3:(dim2+2)*2/3)) = 1; % p(floor(((dim1+2)/3)+1):floor((dim1+2)*2/3)-1,floor((dim2+2)/3)+1:floor((dim2+2)*2/3)-1) = 0; p = bwdist(p)-bwdist(1-p)+im2double(p)-.5; end end 运行后提示 未定义函数或变量 'init'。求大神详细解答ORZ

minst深度学习例程不收敛，成功率始终在十几

minst深度学习程序不收敛 是关于tensorflow的问题。我是tensorflow的初学者。从书上抄了minst的学习程序。但是运行之后，无论学习了多少批次，成功率基本不变。 我做了许多尝试，去掉了正则化，去掉了滑动平均，还是不行。把batch_size改成了2，观察变量运算情况，输入x是正确的，但神经网络的输出y很多情况下在x不一样的情况下y的两个结果是完全一样的。进而softmax的结果也是一样的。百思不得其解，找不到造成这种情况的原因。这里把代码和运行情况都贴出来，请大神帮我找找原因。大过年的，祝大家春节快乐万事如意。 补充一下，进一步的测试表明，不是不能完成训练，而是要到700000轮以上，且最高达到65%左右就不能提高了。仔细看每一步的参数，是regularization值过大10e15以上，一点点减少，前面的训练都在训练它了。这东西我不是很明白。 ``` import struct import numpy as np import matplotlib.pyplot as plt from matplotlib.widgets import Slider, Button import tensorflow as tf import time #把MNIST的操作封装在一个类中，以后用起来方便。 class MyMinst(): def decode_idx3_ubyte(self,idx3_ubyte_file): with open(idx3_ubyte_file, 'rb') as f: print('解析文件：', idx3_ubyte_file) fb_data = f.read() offset = 0 fmt_header = '>iiii' # 以大端法读取4个 unsinged int32 magic_number, num_images, num_rows, num_cols = struct.unpack_from(fmt_header, fb_data, offset) print('idex3 魔数：{}，图片数：{}'.format(magic_number, num_images)) offset += struct.calcsize(fmt_header) fmt_image = '>' + str(num_rows * num_cols) + 'B' images = np.empty((num_images, num_rows*num_cols)) #做了修改 for i in range(num_images): im = struct.unpack_from(fmt_image, fb_data, offset) images[i] = np.array(im)#这里用一维数组表示图片，np.array(im).reshape((num_rows, num_cols)) offset += struct.calcsize(fmt_image) return images def decode_idx1_ubyte(self,idx1_ubyte_file): with open(idx1_ubyte_file, 'rb') as f: print('解析文件：', idx1_ubyte_file) fb_data = f.read() offset = 0 fmt_header = '>ii' # 以大端法读取两个 unsinged int32 magic_number, label_num = struct.unpack_from(fmt_header, fb_data, offset) print('idex1 魔数：{}，标签数：{}'.format(magic_number, label_num)) offset += struct.calcsize(fmt_header) labels = np.empty(shape=[0,10],dtype=float) #神经网络需要把label变成10位float的数组 fmt_label = '>B' # 每次读取一个 byte for i in range(label_num): n=struct.unpack_from(fmt_label, fb_data, offset) labels=np.append(labels,[[0,0,0,0,0,0,0,0,0,0]],axis=0) labels[i][n]=1 offset += struct.calcsize(fmt_label) return labels def __init__(self): #固定的训练文件位置 self.img=self.decode_idx3_ubyte("/home/zhangyl/Downloads/mnist/train-images.idx3-ubyte") self.result=self.decode_idx1_ubyte("/home/zhangyl/Downloads/mnist/train-labels.idx1-ubyte") print(self.result[0]) print(self.result[1000]) print(self.result[25000]) #固定的验证文件位置 self.validate_img=self.decode_idx3_ubyte("/home/zhangyl/Downloads/mnist/t10k-images.idx3-ubyte") self.validate_result=self.decode_idx1_ubyte("/home/zhangyl/Downloads/mnist/t10k-labels.idx1-ubyte") #每一批读训练数据的起始位置 self.train_read_addr=0 #每一批读训练数据的batchsize self.train_batchsize=100 #每一批读验证数据的起始位置 self.validate_read_addr=0 #每一批读验证数据的batchsize self.validate_batchsize=100 #定义用于返回batch数据的变量 self.train_img_batch=self.img self.train_result_batch=self.result self.validate_img_batch=self.validate_img self.validate_result_batch=self.validate_result def get_next_batch_traindata(self): n=len(self.img) #对参数范围适当约束 if self.train_read_addr+self.train_batchsize<=n : self.train_img_batch=self.img[self.train_read_addr:self.train_read_addr+self.train_batchsize] self.train_result_batch=self.result[self.train_read_addr:self.train_read_addr+self.train_batchsize] self.train_read_addr+=self.train_batchsize #改变起始位置 if self.train_read_addr==n : self.train_read_addr=0 else: self.train_img_batch=self.img[self.train_read_addr:n] self.train_img_batch.append(self.img[0:self.train_read_addr+self.train_batchsize-n]) self.train_result_batch=self.result[self.train_read_addr:n] self.train_result_batch.append(self.result[0:self.train_read_addr+self.train_batchsize-n]) self.train_read_addr=self.train_read_addr+self.train_batchsize-n #改变起始位置,这里没考虑batchsize大于n的情形 return self.train_img_batch,self.train_result_batch #测试一下用临时变量返回是否可行 def set_train_read_addr(self,addr): self.train_read_addr=addr def set_train_batchsize(self,batchsize): self.train_batchsize=batchsize if batchsize <1 : self.train_batchsize=1 def set_validate_read_addr(self,addr): self.validate_read_addr=addr def set_validate_batchsize(self,batchsize): self.validate_batchsize=batchsize if batchsize<1 : self.validate_batchsize=1 myminst=MyMinst() #minst类的实例 batch_size=2 #设置每一轮训练的Batch大小 learning_rate=0.8 #初始学习率 learning_rate_decay=0.999 #学习率的衰减 max_steps=300000 #最大训练步数 #定义存储训练轮数的变量，在使用tensorflow训练神经网络时， #一般会将代表训练轮数的变量通过trainable参数设置为不可训练的 training_step = tf.Variable(0,trainable=False) #定义得到隐藏层和输出层的前向传播计算方式，激活函数使用relu() def hidden_layer(input_tensor,weights1,biases1,weights2,biases2,layer_name): layer1=tf.nn.relu(tf.matmul(input_tensor,weights1)+biases1) return tf.matmul(layer1,weights2)+biases2 x=tf.placeholder(tf.float32,[None,784],name="x-input") y_=tf.placeholder(tf.float32,[None,10],name="y-output") #生成隐藏层参数，其中weights包含784*500=39200个参数 weights1=tf.Variable(tf.truncated_normal([784,500],stddev=0.1)) biases1=tf.Variable(tf.constant(0.1,shape=[500])) #生成输出层参数，其中weights2包含500*10=5000个参数 weights2=tf.Variable(tf.truncated_normal([500,10],stddev=0.1)) biases2=tf.Variable(tf.constant(0.1,shape=[10])) #计算经过神经网络前后向传播后得到的y值 y=hidden_layer(x,weights1,biases1,weights2,biases2,'y') #初始化一个滑动平均类，衰减率为0.99 #为了使模型在训练前期可以更新的更快，这里提供了num_updates参数，并设置为当前网络的训练轮数 #averages_class=tf.train.ExponentialMovingAverage(0.99,training_step) #定义一个更新变量滑动平均值的操作需要向滑动平均类的apply()函数提供一个参数列表 #train_variables()函数返回集合图上Graph.TRAINABLE_VARIABLES中的元素。 #这个集合的元素就是所有没有指定trainable_variables=False的参数 #averages_op=averages_class.apply(tf.trainable_variables()) #再次计算经过神经网络前向传播后得到的y值，这里使用了滑动平均，但要牢记滑动平均值只是一个影子变量 #average_y=hidden_layer(x,averages_class.average(weights1), # averages_class.average(biases1), # averages_class.average(weights2), # averages_class.average(biases2), # 'average_y') #softmax,计算交叉熵损失，L2正则，随机梯度优化器，学习率采用指数衰减 #函数原型为sparse_softmax_cross_entropy_with_logits(_sential,labels,logdits,name) #与softmax_cross_entropy_with_logits()函数的计算方式相同，更适用于每个类别相互独立且排斥 #的情况，即每一幅图只能属于一类 #在1.0.0版本的TensorFlow中，这个函数只能通过命名参数的方式来使用，在这里logits参数是神经网 #络不包括softmax层的前向传播结果,lables参数给出了训练数据的正确答案 softmax=tf.nn.softmax(y) cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y+1e-10,labels=tf.argmax(y_,1)) #argmax()函数原型为argmax(input,axis,name,dimension)用于计算每一个样例的预测答案，其中 # input参数y是一个batch_size*10(batch_size行，10列)的二维数组。每一行表示一个样例前向传 # 播的结果，axis参数“1”表示选取最大值的操作只在第一个维度进行。即只在每一行选取最大值对应的下标 # 于是得到的结果是一个长度为batch_size的一维数组，这个一维数组的值就表示了每一个样例的数字识别 # 结果。 regularizer=tf.contrib.layers.l2_regularizer(0.0001) #计算L2正则化损失函数 regularization=regularizer(weights1)+regularizer(weights2) #计算模型的正则化损失 loss=tf.reduce_mean(cross_entropy)#+regularization #总损失 #用指数衰减法设置学习率，这里staircase参数采用默认的False,即学习率连续衰减 learning_rate=tf.train.exponential_decay(learning_rate,training_step, batch_size,learning_rate_decay) #使用GradientDescentOptimizer优化算法来优化交叉熵损失和正则化损失 train_op=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=training_step) #在训练这个模型时，每过一遍数据既需要通过反向传播来更新神经网络中的参数，又需要 # 更新每一个参数的滑动平均值。control_dependencies()用于这样的一次性多次操作 #同样的操作也可以使用下面这行代码完成： #train_op=tf.group(train_step,average_op) #with tf.control_dependencies([train_step,averages_op]): # train_op=tf.no_op(name="train") #检查使用了滑动平均模型的神经网络前向传播结果是否正确 #equal()函数原型为equal(x,y,name)，用于判断两个张量的每一维是否相等。 #如果相等返回True，否则返回False crorent_predicition=tf.equal(tf.argmax(y,1),tf.argmax(y_,1)) #cast()函数的原型为cast(x,DstT,name),在这里用于将一个布尔型的数据转换为float32类型 #之后对得到的float32型数据求平均值，这个平均值就是模型在这一组数据上的正确率 accuracy=tf.reduce_mean(tf.cast(crorent_predicition,tf.float32)) #创建会话和开始训练过程 with tf.Session() as sess: #在稍早的版本中一般使用initialize_all_variables()函数初始化全部变量 tf.global_variables_initializer().run() #准备验证数据 validate_feed={x:myminst.validate_img,y_:myminst.validate_result} #准备测试数据 test_feed= {x:myminst.img,y_:myminst.result} for i in range(max_steps): if i%1000==0: #计算滑动平均模型在验证数据上的结果 #为了能得到百分数输出，需要将得到的validate_accuracy扩大100倍 validate_accuracy= sess.run(accuracy,feed_dict=validate_feed) print("After %d trainning steps,validation accuracy using average model is %g%%" %(i,validate_accuracy*100)) #产生这一轮使用一个batch的训练数据，并进行训练 #input_data.read_data_sets（）函数生成的类提供了train.next_batch()函数 #通过设置函数的batch_size参数就可以从所有的训练数据中读取一个小部分作为一个训练batch myminst.set_train_batchsize(batch_size) xs,ys=myminst.get_next_batch_traindata() var_print=sess.run([x,y,y_,loss,train_op,softmax,cross_entropy,regularization,weights1],feed_dict={x:xs,y_:ys}) print("after ",i," trainning steps:") print("x=",var_print[0][0],var_print[0][1],"y=",var_print[1],"y_=",var_print[2],"loss=",var_print[3], "softmax=",var_print[5],"cross_entropy=",var_print[6],"regularization=",var_print[7],var_print[7]) time.sleep(0.5) #使用测试数据集检验神经网络训练之后的正确率 #为了能得到百分数输出，需要将得到的test_accuracy扩大100倍 test_accuracy=sess.run(accuracy,feed_dict=test_feed) print("After %d training steps,test accuracy using average model is %g%%"%(max_steps,test_accuracy*100)) 下面是运行情况的一部分： x= [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 8. 76. 202. 254. 255. 163. 37. 2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 13. 182. 253. 253. 253. 253. 253. 253. 23. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 15. 179. 253. 253. 212. 91. 218. 253. 253. 179. 109. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 105. 253. 253. 160. 35. 156. 253. 253. 253. 253. 250. 113. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 19. 212. 253. 253. 88. 121. 253. 233. 128. 91. 245. 253. 248. 114. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 104. 253. 253. 110. 2. 142. 253. 90. 0. 0. 26. 199. 253. 248. 63. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 173. 253. 253. 29. 0. 84. 228. 39. 0. 0. 0. 72. 251. 253. 215. 29. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 36. 253. 253. 203. 13. 0. 0. 0. 0. 0. 0. 0. 0. 82. 253. 253. 170. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 36. 253. 253. 164. 0. 0. 0. 0. 0. 0. 0. 0. 0. 11. 198. 253. 184. 6. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 36. 253. 253. 82. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 138. 253. 253. 35. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 128. 253. 253. 47. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 48. 253. 253. 35. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 154. 253. 253. 47. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 48. 253. 253. 35. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 102. 253. 253. 99. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 48. 253. 253. 35. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 36. 253. 253. 164. 0. 0. 0. 0. 0. 0. 0. 0. 0. 16. 208. 253. 211. 17. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 32. 244. 253. 175. 4. 0. 0. 0. 0. 0. 0. 0. 0. 44. 253. 253. 156. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 171. 253. 253. 29. 0. 0. 0. 0. 0. 0. 0. 30. 217. 253. 188. 19. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 171. 253. 253. 59. 0. 0. 0. 0. 0. 0. 60. 217. 253. 253. 70. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 78. 253. 253. 231. 48. 0. 0. 0. 26. 128. 249. 253. 244. 94. 15. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 8. 151. 253. 253. 234. 101. 121. 219. 229. 253. 253. 201. 80. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 38. 232. 253. 253. 253. 253. 253. 253. 253. 201. 66. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 232. 253. 253. 95. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 86. 46. 0. 0. 0. 0. 0. 0. 91. 246. 252. 232. 57. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 103. 252. 187. 13. 0. 0. 0. 0. 22. 219. 252. 252. 175. 0. 0. 0. 0. 0. 0. 0. 0. 0. 10. 0. 0. 0. 0. 8. 181. 252. 246. 30. 0. 0. 0. 0. 65. 252. 237. 197. 64. 0. 0. 0. 0. 0. 0. 0. 0. 0. 87. 0. 0. 0. 13. 172. 252. 252. 104. 0. 0. 0. 0. 5. 184. 252. 67. 103. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 8. 172. 252. 248. 145. 14. 0. 0. 0. 0. 109. 252. 183. 137. 64. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 5. 224. 252. 248. 134. 0. 0. 0. 0. 0. 53. 238. 252. 245. 86. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 12. 174. 252. 223. 88. 0. 0. 0. 0. 0. 0. 209. 252. 252. 179. 9. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 11. 171. 252. 246. 61. 0. 0. 0. 0. 0. 0. 83. 241. 252. 211. 14. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 129. 252. 252. 249. 220. 220. 215. 111. 192. 220. 221. 243. 252. 252. 149. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 144. 253. 253. 253. 253. 253. 253. 253. 253. 253. 255. 253. 226. 153. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 44. 77. 77. 77. 77. 77. 77. 77. 77. 153. 253. 235. 32. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 74. 214. 240. 114. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 24. 221. 243. 57. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 8. 180. 252. 119. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 136. 252. 153. 7. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 136. 251. 226. 34. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 123. 252. 246. 39. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 165. 252. 127. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 165. 175. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 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0. 0. 0. 0. 0. 177. 202. 19. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] y= [[ 0.8592988 0.3954708 -0.77875614 0.26675048 0.19804694 -0.61968666 0.18084174 0.4034736 -0.34189415 0.43645462] [ 0.8592988 0.3954708 -0.77875614 0.26675048 0.19804694 -0.61968666 0.18084174 0.4034736 -0.34189415 0.43645462]] y_= [[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]] loss= 2.2191708 softmax= [[0.19166051 0.12052987 0.0372507 0.10597225 0.09893605 0.04367344 0.09724841 0.12149832 0.05765821 0.12557226] [0.19166051 0.12052987 0.0372507 0.10597225 0.09893605 0.04367344 0.09724841 0.12149832 0.05765821 0.12557226]] cross_entropy= [2.3304868 2.1078548] regularization= 50459690000000.0 50459690000000.0 after 46 trainning steps: x= [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 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0. 0. 0. 0. 0. 0. 3. 175. 253. 253. 72. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 123. 253. 253. 138. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 10. 244. 253. 230. 34. 0. 9. 24. 23. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 181. 253. 249. 123. 0. 69. 195. 253. 249. 146. 15. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 21. 231. 253. 202. 0. 70. 236. 253. 253. 253. 253. 170. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 22. 139. 253. 213. 26. 13. 200. 253. 253. 183. 252. 253. 220. 22. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 72. 253. 253. 129. 0. 86. 253. 253. 129. 4. 105. 253. 253. 70. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 72. 253. 253. 77. 22. 245. 253. 183. 4. 0. 2. 105. 253. 70. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 132. 253. 253. 11. 24. 253. 253. 116. 0. 0. 1. 150. 253. 70. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 189. 253. 241. 10. 24. 253. 253. 59. 0. 0. 82. 253. 212. 30. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 189. 253. 147. 0. 24. 253. 253. 150. 30. 44. 208. 212. 31. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 189. 253. 174. 3. 7. 185. 253. 253. 227. 247. 184. 30. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 150. 253. 253. 145. 95. 234. 253. 253. 253. 126. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 72. 253. 253. 253. 253. 253. 253. 253. 169. 14. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 5. 114. 240. 253. 253. 234. 135. 44. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] y= [[ 0.7093834 0.30119324 -0.80789334 0.1838598 0.12065991 -0.6538477 0.49587095 0.6995347 -0.38699397 0.33823296] [ 0.7093834 0.30119324 -0.80789334 0.1838598 0.12065991 -0.6538477 0.49587095 0.6995347 -0.38699397 0.33823296]] y_= [[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]] loss= 2.2107558 softmax= [[0.16371341 0.10884525 0.03590371 0.09679484 0.09086671 0.04188326 0.1322382 0.16210894 0.05469323 0.11295244] [0.16371341 0.10884525 0.03590371 0.09679484 0.09086671 0.04188326 0.1322382 0.16210894 0.05469323 0.11295244]] cross_entropy= [2.3983614 2.0231504] regularization= 50459690000000.0 50459690000000.0 after 47 trainning steps: x= [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 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0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 32. 229. 253. 255. 144. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 66. 236. 252. 253. 92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 66. 234. 252. 252. 253. 92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 19. 236. 252. 252. 252. 253. 92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 53. 181. 252. 168. 43. 232. 253. 92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 179. 255. 218. 32. 93. 253. 252. 84. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 81. 244. 239. 33. 0. 114. 252. 209. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 207. 252. 237. 70. 153. 240. 252. 32. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 207. 252. 253. 252. 252. 252. 210. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 61. 242. 253. 252. 168. 96. 12. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 68. 254. 255. 254. 107. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 11. 176. 230. 253. 253. 253. 212. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 28. 197. 253. 253. 253. 253. 253. 229. 107. 14. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 194. 253. 253. 253. 253. 253. 253. 253. 253. 53. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 69. 241. 253. 253. 253. 253. 241. 186. 253. 253. 195. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 10. 161. 253. 253. 253. 246. 40. 57. 231. 253. 253. 195. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 140. 253. 253. 253. 253. 154. 0. 25. 253. 253. 253. 195. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 213. 253. 253. 253. 135. 8. 0. 3. 128. 253. 253. 195. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 77. 238. 253. 253. 253. 7. 0. 0. 0. 116. 253. 253. 195. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 11. 165. 253. 253. 231. 70. 1. 0. 0. 0. 78. 237. 253. 195. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 33. 253. 253. 253. 182. 0. 0. 0. 0. 0. 0. 200. 253. 195. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 98. 253. 253. 253. 24. 0. 0. 0. 0. 0. 0. 42. 253. 195. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 253. 24. 0. 0. 0. 0. 0. 0. 163. 253. 195. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 189. 13. 0. 0. 0. 0. 0. 53. 227. 253. 121. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 114. 0. 0. 0. 0. 0. 21. 227. 253. 231. 27. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 114. 0. 0. 0. 5. 131. 143. 253. 231. 59. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 236. 73. 58. 217. 223. 253. 253. 253. 174. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 253. 253. 253. 253. 253. 253. 253. 253. 48. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 149. 253. 253. 253. 253. 253. 253. 253. 253. 182. 15. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 12. 168. 253. 253. 253. 253. 253. 248. 89. 23. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] y= [[ 0.5813921 0.21609789 -0.8359629 0.10818548 0.44052082 -0.6865921 0.78338754 0.5727978 -0.4297532 0.24992661] [ 0.5813921 0.21609789 -0.8359629 0.10818548 0.44052082 -0.6865921 0.78338754 0.5727978 -0.4297532 0.24992661]] y_= [[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]] loss= 2.452383 softmax= [[0.14272858 0.09905256 0.03459087 0.08892009 0.1239742 0.04016358 0.1746773 0.14150718 0.05192496 0.10246069] [0.14272858 0.09905256 0.03459087 0.08892009 0.1239742 0.04016358 0.1746773 0.14150718 0.05192496 0.10246069]] cross_entropy= [2.9579558 1.9468105] regularization= 50459690000000.0 50459690000000.0 已终止 ```

关于matlab提取视频中运动目标的问题

想用MATLAB做一个提取运动目标的功能，如图 代码如下但是现象极其糟糕 不知道错误处在哪里 请各位大神指教 ``` fileName = 'video.avi'; obj = VideoReader(fileName);%输入视频位置 numFrames = obj.NumberOfFrames;% 帧的总数 for k = 1 : 206% 读取帧 frame = read(obj,k);%读取第几帧 frame1=read(obj, k+1); frame2=read(obj, k+2); grayframe=rgb2gray(frame); %彩色转化成灰度图 grayframe1 = rgb2gray(frame1);%灰度化 grayframe2=rgb2gray(frame2); difgrayframe= grayframe - grayframe1;%邻帧差 difgrayframe2= grayframe1 - grayframe2;%邻帧差 level1 = graythresh(difgrayframe); level2 = graythresh(difgrayframe2); fdiff1=im2bw(abs(difgrayframe),level1);%与阈值比较转换成二值图像 fdiff2=im2bw(abs(difgrayframe2),level2);%与阈值比较转换成二值图像 f= fdiff1&fdiff2; %得到移动的区域 pause(0.01); k1=medfilt2(f,[3,3]); %进行3*3模板中值滤波 k2 = bwmorph(k1,'close'); %闭运算 figure(1); imshow(k2); end ```

求助帖！！！ubuntu18.04 安装opencv4.0.0源码无限次出现如下问题

./src/image_opencv.cpp: In function ‘IplImage* image_to_ipl(image)’: ./src/image_opencv.cpp:16:5: error: ‘IPlImage’ was not declared in this scope IPlImage *disp = cvCreateImage(cvSize(im.w,im.h), IPL_DEPTH_8U, im.c); ^~~~~~~~ compilation terminated due to -Wfatal-errors. Makefile:86: recipe for target 'obj/image_opencv.o' failed make: *** [obj/image_opencv.o] Error 1 make: *** Waiting for unfinished jobs.... 但我在src文件夹中的image_opencv.cpp中也引入了#include "opencv2/imgproc/imgproc_c.h" 求助实在不知道怎么办了

springboot+WebSocke开发IM系统遇见的问题？

开发IM系统用于多商家平台不同客服和交友类的小程序，那么我后端使用WebSocket写了IM功能，做到了推送，用户之间可以给用户发信息聊天，但是有一个问题不明白，接收信息的用户未必在线，那么推送就会失败，这个时候，推送的信息是保存在哪里的？redis还是数据库？然后才等待接收者上线时候，查询一遍要不要待推送的，然后推送给他。

c语言编程时出现错误为：1.#QNAN0000000，麻烦大佬们看一下问题出在哪儿了，感谢

 # 数值分析大作业 ## c语言 这个是数值分析大作业，我是按照书上一步一步编写的，前366行都是没问题的，问题就出在双步位移QR分解那里，但是我真的无能为力了，感谢大佬们相救，感谢。 ``` 代码如下：（有些printf是我测试用的） #include<stdio.h> #include<math.h> const int n=10,L=10; double err=1e-12; //定义主函数 int main() { int i,j; double A[n][n],B[n][n]; //B[][]是为保证矩阵A(n-1)[][]不被破坏的中间矩阵 //调用函数声明 void faketriangle(double a[n][n]); //构造拟上三角化矩阵函数 void QR(double a[n][n]); void doublestepQR(double a[n][n]); printf("矩阵A为：\n"); //定义矩阵A for(i=0;i<n;i++) { for(j=0;j<n;j++) { if(i==j) { A[i][j]=1.52*cos(1+i+1.2*(1+j)); }else { A[i][j]=sin(0.5*(1+i)+0.2*(1+j)); } printf("%0.12f\t",A[i][j]); } printf("\n"); } printf("\n"); printf("矩阵A(n-1)为：\n"); //调用构造拟上三角化矩阵函数A(n-1) faketriangle(A); for(i=0;i<n;i++) { for(j=0;j<n;j++) { B[i][j]=A[i][j]; } } printf("\n"); printf("矩阵A(n-1)分解为QR矩阵\n"); QR(A); printf("\n"); doublestepQR(B); } //构造拟上三角化矩阵函数A(n-1) 函数主体 void faketriangle(double a[n][n]) { int i,j,r; double sum,d,c,h,t; double u[n],w[n],q[n],p[n]; //算法开始迭代 for(r=0;r<n-2;r++) { sum=0; for(i=r+2;i<n;i++) { sum+=fabs(a[i][r]); } //判断是否满足a[i][r]=0(i=r+2,..,n)？ if(sum>0) { sum=0; //计算d for(i=r+1;i<n;i++) { sum+=a[i][r]*a[i][r]; } d=sqrt(sum); //计算c if(a[r+1][r]>0) { c=-d; }else { c=d; } //计算h h=c*c-c*a[r+1][r]; //向量u[r]的建立 for(i=0;i<n;i++) { if(i<r+1) { u[i]=0; }else if(i==r+1) { u[i]=a[i][r]-c; }else { u[i]=a[i][r]; } // printf("%0.12f\n",u[i]); } //求解向量p for(i=0;i<n;i++) { sum=0; for(j=0;j<n;j++) { sum+=a[j][i]*u[j]; } p[i]=sum/h; // printf("%0.12f\n",p[i]); } // //求解矩阵q for(i=0;i<n;i++) { sum=0; for(j=0;j<n;j++) { sum+=a[i][j]*u[j]; } q[i]=sum/h; // printf("%0.12f\n",q[i]); } //求t sum=0; for(i=0;i<n;i++) { sum+=p[i]*u[i]; } t=sum/h; // printf("%0.12f\n",t); //求w[] for(i=0;i<n;i++) { w[i]=q[i]-t*u[i]; // printf("%0.12f\n",w[l]); } //求a(r+1) for(i=0;i<n;i++) { for(j=0;j<n;j++) { a[i][j]=a[i][j]-(w[i]*u[j]+u[i]*p[j]); // printf("%0.12f\t",a[i][j]); } // printf("\n"); } } } //输出矩阵A(n-1) for(i=0;i<n;i++) { for(j=0;j<n;j++) { printf("%0.12f\t",a[i][j]); } printf("\n"); } } //将 A(n-1)矩阵进行QR分解 void QR(double a[n][n]) { int i,j,k,r; double sum,d,c,h; double Q[n][n],u[n],w[n],p[n],R[n][n],b[n][n]; //定义矩阵Q[][] for(i=0;i<n;i++) { for(j=0;j<n;j++) { if(i==j) { Q[i][j]=1; }else { Q[i][j]=0; } // printf("%0.12f\t",Q[i][j]); } // printf("\n"); } // printf("\n"); //算法迭代 for(r=0;r<n-1;r++) { sum=0; for(i=r+1;i<n;i++) { sum+=fabs(a[i][r]); } if(sum>0) { //计算d sum=0; for(i=r;i<n;i++) { sum+=a[i][r]*a[i][r]; } d=sqrt(sum); //计算c if(a[r][r]>0) { c=-d; }else { c=d; } //计算h h=c*(c-a[r][r]); //构造向量u[] for(i=0;i<n;i++) { if(i<r) { u[i]=0; }else if(i==r) { u[i]=a[i][r]-c; }else { u[i]=a[i][r]; } } //计算w[] for(i=0;i<n;i++) { sum=0; for(j=0;j<n;j++) { sum+=Q[i][j]*u[j]; } w[i]=sum; } //计算Q(r+1) for(i=0;i<n;i++) { for(j=0;j<n;j++) { Q[i][j]=Q[i][j]-w[i]*u[j]/h; } } //计算P[] for(j=0;j<n;j++) { sum=0; for(i=0;i<n;i++) { sum+=a[i][j]*u[i]/h; } p[j]=sum; } //计算a[r+1] for(i=0;i<n;i++) { for(j=0;j<n;j++) { a[i][j]=a[i][j]-u[i]*p[j]; } } } } printf("Q矩阵为：\n"); //输出Q[][]、R[][] for(i=0;i<n;i++) { for(j=0;j<n;j++) { printf("%0.12f\t",Q[i][j]); } printf("\n"); } printf("\n"); printf("R矩阵为：\n"); for(i=0;i<n;i++) { for(j=0;j<n;j++) { R[i][j]=a[i][j]; printf("%0.12f\t",R[i][j]); } printf("\n"); } printf("RQ相乘后的矩阵为：\n"); //求解R[][]*Q[][] for(i=0;i<n;i++) { for(j=0;j<n;j++) { sum=0; for(k=0;k<n;k++) { sum+=R[i][k]*Q[k][j]; } b[i][j]=sum; printf("%0.12f\t",b[i][j]); } printf("\n"); } // printf("QR相乘后的矩阵为：\n"); // //求解Q[][]*R[][] // for(i=0;i<n;i++) // { // for(j=0;j<n;j++) // { // sum=0; // for(k=0;k<n;k++) // { // sum+=Q[i][k]*R[k][j]; // } // b[i][j]=sum; // printf("%0.12f\t",b[i][j]); // } // printf("\n"); // } } //以上没问题 //定义复数结构体 struct complex { double re; double im; }; //对A(n-1)进行双步位移的QR分解 void doublestepQR(double a[n][n]) { //M[][]QR分解函数声明 void MQR(double a[n][n],double M[n][n],int m); int i,j,k,m,r,l; double A[n][n],M[n][n],I[n][n],A2[n][n]; double x,y,z,sum; double s,t; struct complex lambda[n]; struct complex s1,s2; k=0;m=n-1; step3: if(fabs(a[m][m-1])<=err) { lambda[m].re=a[m][m]; lambda[m].im=0; m--; goto step4; }else { goto step5; } step4: if(m==0) { lambda[m].re=a[0][0]; lambda[m].im=0; goto step11; }else if(m==-1) { goto step11; }else { goto step3; } step5: x=a[m-1][m-1]+a[m][m]; y=a[m-1][m-1]*a[m][m]-a[m-1][m]*a[m][m-1]; z=x*x-4*y; if(z>=0) { z=sqrt(z); s1.re=(x+z)/2; s1.im=0; s2.re=(x-z)/2; s2.im=0; }else { z=sqrt(fabs(z)); s1.re=(x)/2; s1.im=(z)/2; s2.re=x/2; s2.im=(-z)/2; } step6: if(m==1) { lambda[m].re=s1.re; lambda[m].im=0; lambda[m-1].re=s2.re; lambda[m-1].im=s2.im; goto step11; }else { goto step7; } step7: if(fabs(a[m-1][m-2])<=err) { if(z>=0) { lambda[m-1].re=(x+sqrt(z))/2; //两个特征值 lambda[m-1].im=0; lambda[m-2].re=(x-sqrt(z))/2; lambda[m-2].im=0; }else { lambda[m-1].re=(x)/2; lambda[m-1].im=(sqrt(fabs(z)))/2; lambda[m-2].re=x/2; lambda[m-2].im=(-sqrt(fabs(z)))/2; } m=m-2; goto step4; }else { goto step8; } step8: if(k==L) { goto step12; }else { goto step9; } step9: // for(i=0;i<m;i++) // { // for(j=0;j<m;j++) // { // a[i][j]=a[i][j]; // } // } s=a[m-1][m-1]+a[m][m]; t=a[m-1][m-1]*a[m][m]-a[m][m-1]*a[m-1][m]; //定义矩阵I[][] for(i=0;i<m;i++) { for(j=0;j<m;j++) { if(i==j) { I[i][j]=1; }else { I[i][j]=0; } } } // printf("%M[][]为：\n"); //计算矩阵M[][] for(i=0;i<m;i++) { for(j=0;j<m;j++) { sum=0; for(l=0;l<m;l++) { sum+=a[i][l]*a[l][j]; } M[i][j]=sum-s*a[i][j]+t*I[i][j]; printf("%0.12f\t",M[i][j]); } printf("\n"); } printf("\n"); //调用M[][]QR分解、计算A(k+1)的函数 MQR(a,M,m); step10: k++; goto step3; step11: printf("特征值已计算完毕。\n"); for(i=0;i<n;i++) { printf("%0.12f+%0.12fi\n",lambda[i].re,lambda[i].im); } for(i=0;i<n;i++) { if(lambda[i].im==0) { // vector(); } } step12: printf("未得到所有特征值。\n"); } //M[][]QR分解、计算A(k+1)的函数 void MQR(double a[n][n],double M[n][n],int m) { int i,j,r; double sum,d,c,h,t; double B[n][n],C[n][n],p[n],q[n],v[n],w[n],u[n]; printf("B:\n"); for(i=0;i<m;i++) { for(j=0;j<m;j++) { B[i][j]=M[i][j]; printf("%f\t",B[i][j]); } printf("\n"); } for(i=0;i<m;i++) { for(j=0;j<m;j++) { C[i][j]=a[i][j]; } } //循环计算矩阵A(k+1) for(r=0;r<m;r++) //r的范围 { sum=0; for(i=r+1;i<m;i++) { sum+=fabs(B[i][r]); } printf("sum:\n"); printf("%0.12f\n",sum); //sum有问题 printf("\n\n\n"); printf("r:%d\n\n\n\n\n",r); if(sum>0) { //计算d sum=0; for(i=r;i<m+1;i++) { sum+=B[i][r]*B[i][r]; printf("B:%0.12f\n",B[i][r]); //B[][]? printf("\n"); } printf("sum:%0.12f\n",sum); //sum有问题 fiest!! printf("\n"); d=sqrt(sum); printf("%0.12f\n",d); //d有问题 printf("\n"); //计算c if(B[r][r]>0) { c=-d; }else { c=d; } printf("%0.12f\n",c); //c有问题 printf("\n"); //计算h h=c*(c-B[r][r]); printf("%0.12f\n",h); //h有问题 printf("\n"); //构造向量u[] for(i=0;i<m;i++) { if(i<r) { u[i]=0; }else if(i==r) { u[i]=B[i][r]-c; }else { u[i]=B[i][r]; } } //计算v[] for(i=0;i<m;i++) { sum=0; for(j=r;j<m;j++) { sum+=B[j][i]*u[j]; } v[i]=sum/h; } //计算B(r+1) for(i=0;i<m;i++) { for(j=0;j<m;j++) { B[i][j]=B[i][j]-u[i]*v[j]; } } //计算p[] for(i=0;i<m;i++) { sum=0; for(j=r;j<m;j++) { sum+=C[j][i]*u[j]; } p[i]=sum/h; } //计算q[] for(i=0;i<m;i++) { sum=0; for(j=r;j<m;j++) { sum+=C[i][j]*u[j]; } q[i]=sum/h; } //计算t sum=0; for(i=r;i<m;i++) { sum+=p[i]*u[i]; } t=sum/h; //计算w[] for(i=0;i<m;i++) { w[i]=q[i]-t*u[i]; } //计算C[r+1] for(i=0;i<m;i++) { for(j=0;j<m;j++) { C[i][j]=C[i][j]-w[i]*u[j]-u[i]*p[j]; } } }else { ; } } for(i=0;i<m;i++) { for(j=0;j<m;j++) { a[i][j]=C[i][j]; printf("%f\t",a[i][j]); } printf("\n"); } } ```

MATLAB的function文件

我下面的MATLAB程序，我想输出三个结果，为啥结果只会出现一个，就是b,不会出现其他的结果呢？不知道是什么原因，请大神帮帮我，谢谢了。 function[b,e,h]=lvboshibie(a) a=imread('lena.bmp'); %figure;imshow(a); %title('原图像'); a=im2double(a);%将数变为double型数 b=imnoise(a,'gaussian',0.015); C=im2double(b); F=im2col(C,[3,3],'distinct');%将加躁图像分割成3*3的小窗口 [i,j]=size(F); m=zeros(i,j); n=size(i,j); for i=1:9 for j=1:5180 m(i,j)=F(i,j)-F(5,j);%用3*3的窗口中的中心值对窗口中的数据求出估计的受躁程度 m(5,j)=0.001; n(i,j)=m(i,j)./F(5,j); end end %为用accumarray函数把数据变回原来的排列方式做准备 %制造c矩阵 K=[1 1 0;1 2 0;1 3 0;2 1 0;2 2 0;2 3 0;3 1 0;3 2 0;3 3 0]; A=K; for ii=1:5179; A=[A;K]; end g=zeros(1,5180); F=[1:5180]; F=[g,g,F]; F=[F;F;F;F;F;F;F;F;F]; F=reshape(F,[46620,3]); c=A+F; val=reshape(n,[46620,1]); A=accumarray(c,val);%使用此函数把原来用im2col变成9*5180的矩阵变为一个个3*3的小矩阵 D=reshape(A,[3,15540]); %将矩阵变为原来的222*210的形式 M = []; B=[]; for i = 1:3:15538 temp=D(:,i:i+2); M = [M; temp]; % 使矩阵变为15540*3 end for j=1:222:15539 TEMP=M(j:j+221,:); B = [B, TEMP]; % 使矩阵变为220*210 end %使矩阵为222*208 B=B(1:222,1:208); E=B+100; d=100.*a;%求出估计的灰度时用数据 r=d./E; e=im2uint8(r); [m,n]=size(e); for i=1:m for j=1:n if (e(i,j)>=0)&&(e(i,j)<=50) u=23; q=23/3; v(i,j)=e(i,j)-u; V(1)=2*q^2; elseif (e(i,j)>=51)&&(e(i,j)<=100) u=83; q=47/3; v(i,j)=e(i,j)-u; V(2)=2*q^2; else u=117; q=138/3; v(i,j)=e(i,j)-u; V(3)=2*q^2; end end end v=im2double(v); Q=v.^2; for i=1:m for j=1:n if (e(i,j)>=0)&&(e(i,j)<=50) H(i,j)=im2double(Q(i,j)/V(1)); N(i,j)=exp(-H(i,j)); elseif (e(i,j)>=51)&&(e(i,j)<=100) H(i,j)=im2double(Q(i,j)/V(2)); N(i,j)=exp(-H(i,j)); else H(i,j)=im2double(Q(i,j)/V(3)); N(i,j)=exp(-H(i,j)); end end end s=ones(224,210); l=s*26; l(2:223,2:209)=e; o=zeros(224,210); o(2:223,2:209)=N; [m,n]=find(l<=25); q=size(m); for i=1:q x(m(i),n(i))=l(m(i)-1,n(i)-1)*o(m(i)-1,n(i)-1)+l(m(i)-1,n(i))*o(m(i)-1,n(i))+l(m(i)-1,n(i)+1)*o(m(i)-1,n(i)+1)+l(m(i),n(i)-1)*o(m(i),n(i)-1)+l(m(i),n(i))*o(m(i),n(i))+l(m(i),n(i)+1)*o(m(i),n(i)+1)+l(m(i)+1,n(i)-1)*o(m(i),n(i)-1)+l(m(i)+1,n(i))*o(m(i)+1,n(i))+l(m(i)+1,n(i)+1)*o(m(i)+1,n(i)+1); y(m(i),n(i))=o(m(i)+1,n(i))+o(m(i),n(i))+o(m(i)-1,n(i))+o(m(i)+1,n(i)-1)+o(m(i)-1,n(i)-1)+o(m(i),n(i)-1)+o(m(i)-1,n(i)+1)+o(m(i),n(i)+1)+o(m(i)+1,n(i)+1); l(m(i),n(i))=x(m(i),n(i))/y(m(i),n(i)); end h=l(2:223,2:209); h=round(h); %figure,imshow(h,[]); %title('第二次去噪效果'); b e h end

有大佬帮忙看一下这个死锁怎么形成的吗

按道理主键+RR不会形成死锁啊 ``` InnoDB: * WE ROLL BACK TRANSACTION (1) InnoDB: * (2) WAITING FOR THIS LOCK TO BE GRANTED: InnoDB: * (2) HOLDS THE LOCK(S): InnoDB: * (2) TRANSACTION: InnoDB: InnoDB: * (1) WAITING FOR THIS LOCK TO BE GRANTED: InnoDB: Transactions deadlock detected, dumping detailed information. update examinees set token = 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJodHRwOlwvXC9leGFtLmNvZGVwa3UuY29tXC9hcGlcL2V4YW1pbmVlXC9sb2dpbiIsImlhdCI6MTU3NjM3MjE4MCwiZXhwIjoxNjEyMzcyMTgwLCJuYmYiOjE1NzYzNzIxODAsImp0aSI6Im15TGZ6NmhqVFJzYTNjb1EiLCJzdWIiOjg4NCwicHJ2IjoiNDY3MGJjYmM4YzU5NTJjZTdmN2ExZjQ4OTliOTU0YzU1YjU2NDMyZCJ9.pIIe2xY-eh_3_HhUOURdj325sq9Od1koOVPlRKZ6hXM', login_count = 7, last_login_at = '2019-12-15 09:09:40', examinees.updated_at = '2019-12-15 09:09:40' where id = 884 1: len 6; hex 0000003d236f; asc =#o;; 5: len 15; hex 38353836343036304071712e636f6d; asc 85864060@qq.com;; 14: len 30; hex 68747470733a2f2f6578616d2d313235333338363431342e66696c652e6d; asc https://exam-1253386414.file.m; (total 92 bytes); 10: len 18; hex 343431333233323030383130303333343131; asc 441323200810033411;; 14: len 30; hex 68747470733a2f2f6578616d2d313235333338363431342e66696c652e6d; asc https://exam-1253386414.file.m; (total 92 bytes); RECORD LOCKS space id 301 page no 83 n bits 88 index PRIMARY of table exam.examinees trx id 4006767 lock_mode X locks rec but not gap waiting Record lock, heap no 12 PHYSICAL RECORD: n_fields 27; compact format; info bits 0 3: len 9; hex e69d8ee59bbde7909b; asc ;; 25: len 4; hex 5d68cbfc; asc ]h ;; 18: len 24; hex e883a1e88081e5b888e280ad203135333237323433333934; asc 15327243394;; 7: len 30; hex 24327924313024526e656676315a48716755427072383455456c71732e45; asc $2y$10$Rnefv1ZHqgUBpr84UElqs.E; (total 60 bytes); mysql tables in use 1, locked 1 LOCK WAIT 19 lock struct(s), heap size 1136, 8 row lock(s), undo log entries 224 3: len 6; hex e69cb1e888aa; asc ;; 7: len 30; hex 243279243130247756783376524c2e38756457306945644f6e5061734f53; asc $2y$10$wVx3vRL.8udW0iEdOnPasOS; (total 60 bytes); * (1) TRANSACTION: 0: len 4; hex 00000374; asc t;; 13: len 0; hex ; asc ;; 22: len 4; hex 00000008; asc ;; 23: len 4; hex 5df58817; asc ] ;; Record lock, heap no 19 PHYSICAL RECORD: n_fields 27; compact format; info bits 0 8: len 1; hex 01; asc ;; 18: len 24; hex e883a1e88081e5b888e280ad203135333237323433333934; asc 15327243394;; 11: len 1; hex 81; asc ;; 13: len 0; hex ; asc ;; 16: len 1; hex 81; asc ;; 20: len 4; hex 00000000; asc ;; 22: len 4; hex 00000004; asc ;; 24: SQL NULL; 6: len 11; hex 3133383032353838323832; asc 13802588282;; 21: len 30; hex 65794a30655841694f694a4b563151694c434a68624763694f694a49557a; asc eyJ0eXAiOiJKV1QiLCJhbGciOiJIUz; (total 337 bytes); 24: SQL NULL; TRANSACTION 4006767, ACTIVE 1426 sec starting index read 19: len 4; hex 00000013; asc ;; RECORD LOCKS space id 301 page no 28 n bits 88 index PRIMARY of table exam.examinees trx id 4005686 lock_mode X locks rec but not gap waiting Record lock, heap no 19 PHYSICAL RECORD: n_fields 27; compact format; info bits 0 MySQL thread id 10516222, OS thread handle 139667263854336, query id 300969200 *.190 exam updating 10: len 18; hex 343431333233323030383130303333343131; asc 441323200810033411;; 11: len 1; hex 81; asc ;; 12: len 4; hex 00000000; asc ;; 16: len 1; hex 81; asc ;; mysql tables in use 1, locked 1 MySQL thread id 10520696, OS thread handle 139667256985344, query id 301142683 *.190 exam updating 26: len 4; hex 5df58817; asc ] ;; 3: len 6; hex e69cb1e888aa; asc ;; 13: len 0; hex ; asc ;; 10: len 18; hex 343430333034323030383032303230343334; asc 440304200802020434;; 12: len 4; hex 00000000; asc ;; 17: len 1; hex 01; asc ;; 15: len 10; hex 323030382d31302d3033; asc 2008-10-03;; 19: len 4; hex 00000013; asc ;; 20: len 4; hex 00000000; asc ;; 7: len 30; hex 243279243130247756783376524c2e38756457306945644f6e5061734f53; asc $2y$10$wVx3vRL.8udW0iEdOnPasOS; (total 60 bytes); 9: len 30; hex 5b2268747470733a5c2f5c2f6578616d2d313235333338363431342e6669; asc ["https://exam-1253386414.fi; (total 205 bytes); 28 lock struct(s), heap size 3520, 28 row lock(s), undo log entries 1850 26: len 4; hex 5df58817; asc ] ;; 23: len 4; hex 5df5863f; asc ] ?;; 17: len 1; hex 02; asc ;; 26: len 4; hex 5df5863f; asc ] ?;; RECORD LOCKS space id 301 page no 28 n bits 88 index PRIMARY of table exam.examinees trx id 4006767 lock_mode X locks rec but not gap 0: len 4; hex 00000374; asc t;; 4: len 9; hex e69e97e7bfa0e78fa0; asc ;; 15: len 10; hex 323030382d30322d3032; asc 2008-02-02;; TRANSACTION 4005686, ACTIVE 1709 sec starting index read 2: len 7; hex 29000001cf11da; asc ) ;; 8: len 1; hex 01; asc ;; 4: len 9; hex e69e97e7bfa0e78fa0; asc ;; 5: len 15; hex 38353836343036304071712e636f6d; asc 85864060@qq.com;; 9: len 30; hex 5b2268747470733a5c2f5c2f6578616d2d313235333338363431342e6669; asc ["https://exam-1253386414.fi; (total 205 bytes); 18: len 24; hex e883a1e88081e5b888e280ad203135333237323433333934; asc 15327243394;; 6: len 11; hex 3138313239393331303638; asc 18129931068;; 14: len 30; hex 68747470733a2f2f6578616d2d313235333338363431342e66696c652e6d; asc https://exam-1253386414.file.m; (total 92 bytes); update examinees set token = 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJodHRwOlwvXC9leGFtLmNvZGVwa3UuY29tXC9hcGlcL2V4YW1pbmVlXC9sb2dpbiIsImlhdCI6MTU3NjM3MzQ1NCwiZXhwIjoxNjEyMzczNDU0LCJuYmYiOjE1NzYzNzM0NTQsImp0aSI6IlhXWmZNSUdNTjVLSTd6U0kiLCJzdWIiOjE2MjYsInBydiI6IjQ2NzBiY2JjOGM1OTUyY2U3ZjdhMWY0ODk5Yjk1NGM1NWI1NjQzMmQifQ.zUGTJnfnGREKzbkASKjVtPVaac_3bMXXUVuDs7vdDKE', login_count = 4, last_login_at = '2019-12-15 09:30:54', examinees.updated_at = '2019-12-15 09:30:54' where id = 1626 1: len 6; hex 0000003d236f; asc =#o;; 2: len 7; hex 29000001cf11da; asc ) ;; 25: len 4; hex 5d68cbfc; asc ]h ;; 6: len 11; hex 3138313239393331303638; asc 18129931068;; 15: len 10; hex 323030382d31302d3033; asc 2008-10-03;; 23: len 4; hex 5df58817; asc ] ;; 0: len 4; hex 0000065a; asc Z;; 11: len 1; hex 81; asc ;; 12: len 4; hex 00000000; asc ;; 15: len 10; hex 323030382d31302d3033; asc 2008-10-03;; 23: len 4; hex 5df58817; asc ] ;; 0: len 4; hex 0000065a; asc Z;; 9: len 30; hex 5b2268747470733a5c2f5c2f736372617463682d776f726b732d31323533; asc ["https://scratch-works-1253; (total 215 bytes); 21: len 30; hex 65794a30655841694f694a4b563151694c434a68624763694f694a49557a; asc eyJ0eXAiOiJKV1QiLCJhbGciOiJIUz; (total 337 bytes); 16: len 1; hex 81; asc ;; 21: len 30; hex 65794a30655841694f694a4b563151694c434a68624763694f694a49557a; asc eyJ0eXAiOiJKV1QiLCJhbGciOiJIUz; (total 339 bytes); 17: len 1; hex 01; asc ;; 4: len 6; hex e983ade99c9e; asc ;; 5: len 15; hex 31323038383438374071712e636f6d; asc 12088487@qq.com;; 19: len 4; hex 00000013; asc ;; 24: SQL NULL; 2: len 7; hex 38000001a3284a; asc 8 (J;; 20: len 4; hex 00000000; asc ;; 25: len 4; hex 5dea312c; asc ] 1,;; ```

请问matlab里自带的deconvblind函数运用的是哪种盲解卷积的算法，求原理过程~~~

% Parse inputs to verify valid function calling syntaxes and arguments [J,P,NUMIT,DAMPAR,READOUT,WEIGHT,sizeI,classI,sizePSF,FunFcn,FunArg] = ... parse_inputs(varargin{:}); % 1. Prepare parameters for iterations % % Create indexes for image according to the sampling rate idx = repmat({':'},[1 length(sizeI)]); wI = max(WEIGHT.*(READOUT + J{1}),0);% at this point - positivity constraint fw = fftn(WEIGHT); clear WEIGHT; DAMPAR22 = (DAMPAR.^2)/2; % 2. L_R Iterations % lambda = 2*any(J{4}(:)~=0); for k = (lambda + 1) : (lambda + NUMIT), % 2.a Make an image and PSF predictions for the next iteration if k > 2,% image lambda = (J{4}(:,1).'*J{4}(:,2))/(J{4}(:,2).'*J{4}(:,2) + eps); lambda = max(min(lambda,1),0); % stability enforcement lambda(0,1)之间 end Y = max(J{2} + lambda*(J{2} - J{3}),0);% image positivity constraint if k > 2,% PSF lambda = (P{4}(:,1).'*P{4}(:,2))/(P{4}(:,2).'*P{4}(:,2) + eps); lambda = max(min(lambda,1),0);% stability enforcement end B = max(P{2} + lambda*(P{2} - P{3}),0);% PSF positivity constraint sumPSF = sum(B(:)); B = B/(sum(B(:)) + (sumPSF==0)*eps);% normalization is a necessary constraint, % because given only input image, the algorithm cannot even know how much % power is in the image vs PSF. Therefore, we force PSF to satisfy this % type of normalization: sum to one. % 2.b Make core for the LR estimation CC = corelucy(Y,psf2otf(B,sizeI),DAMPAR22,wI,READOUT,1,idx,[],[]); % 2.c Determine next iteration image & apply positivity constraint J{3} = J{2}; H = psf2otf(P{2},sizeI); scale = real(ifftn(conj(H).*fw)) + sqrt(eps); J{2} = max(Y.*real(ifftn(conj(H).*CC))./scale,0); clear scale; J{4} = [J{2}(:)-Y(:) J{4}(:,1)]; clear Y H; % 2.d Determine next iteration PSF & apply positivity constraint + normalization P{3} = P{2}; H = fftn(J{3}); scale = otf2psf(conj(H).*fw,sizePSF) + sqrt(eps); P{2} = max(B.*otf2psf(conj(H).*CC,sizePSF)./scale,0); clear CC H; sumPSF = sum(P{2}(:)); P{2} = P{2}/(sumPSF + (sumPSF==0)*eps); if ~isempty(FunFcn), FunArg{1} = P{2}; P{2} = feval(FunFcn,FunArg{:}); end; P{4} = [P{2}(:)-B(:) P{4}(:,1)]; end clear fw wI; % 3. Convert the right array (for cell it is first array, for notcell it is % second array) to the original image class & output the whole num = 1 + strcmp(classI{1},'notcell'); if ~strcmp(classI{2},'double'), J{num} = images.internal.changeClass(classI{2},J{num}); end if num == 2,% the input & output is NOT a cell P = P{2}; J = J{2}; end; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Function: parse_inputs function [J,P,NUMIT,DAMPAR,READOUT,WEIGHT,sizeI,classI,sizePSF,FunFcn,FunArg] ... = parse_inputs(varargin) % % Outputs: % I=J{1} the input array (could be any numeric class, 2D, 3D) % P=P{1} the operator that distorts the ideal image % % Defaults: % NUMIT=[];NUMIT_d = 10; % Number of iterations, usually produces good % result by 10. DAMPAR =[];DAMPAR_d = 0;% No damping is default WEIGHT =[]; % All pixels are of equal quality, flat-field is one READOUT=[];READOUT_d= 0;% Zero readout noise or any other % back/fore/ground noise associated with CCD camera. % Or the Image is corrected already for this noise by user. FunFcn = '';FunFcn_d = ''; FunArg = {};FunArg_d = {}; funnum = [];funnum_d = nargin+1; narginchk(2,inf);% no constraint on max number % because of FUN args % First, assign the inputs starting with the cell/not cell image & PSF switch iscell(varargin{1}) + iscell(varargin{2}), case 0, % no-cell array is used to do a single set of iterations classI{1} = 'notcell'; J{1} = varargin{1};% create a cell array in order to do the iterations P{1} = varargin{2}; case 1, error(message('images:deconvblind:IandInitpsfMustBeOfSameType')) case 2,% input cell is used to resume the interrupted iterations or classI{1} = 'cell';% to interrupt the iteration to resume them later J = varargin{1}; P = varargin{2}; if length(J)~=length(P), error(message('images:deconvblind:IandInitpsfMustBeOfSameSize')) end end; % check the Image, which is the first array of the J-cell [sizeI, sizePSF] = padlength(size(J{1}), size(P{1})); classI{2} = class(J{1}); validateattributes(J{1},{'uint8' 'uint16' 'double' 'int16' 'single'},... {'real' 'nonempty' 'finite'},mfilename,'I',1); if prod(sizeI)<2, error(message('images:deconvblind:inputImageMustHaveAtLeast2Elements')) elseif ~isa(J{1},'double'), J{1} = im2double(J{1}); end % check the PSF, which is the first array of the P-cell validateattributes(P{1},{'uint8' 'uint16' 'double' 'int16' 'single'},... {'real' 'nonempty' 'finite' 'nonzero'},mfilename,'INITPSF',2); if prod(sizePSF)<2, error(message('images:deconvblind:initPSFMustHaveAtLeast2Elements')) elseif ~isa(P{1},'double'), P{1} = double(P{1}); end % now since the image&PSF are OK&double, we assign the rest of the J & P cells len = length(J); if len == 1,% J = {I} will be reassigned to J = {I,I,0,0} J{2} = J{1}; J{3} = 0; J{4}(prod(sizeI),2) = 0; P{2} = P{1}; P{3} = 0; P{4}(prod(sizePSF),2) = 0; elseif len ~= 4,% J = {I,J,Jm1,gk} has to have 4 or 1 arrays error(message('images:deconvblind:inputCellsMustBe1or4ElementNumArrays')) else % check if J,Jm1,gk are double in the input cell if ~all([isa(J{2},'double'),isa(J{3},'double'),isa(J{4},'double')]), error(message('images:deconvblind:ImageCellElementsMustBeDouble')) elseif ~all([isa(P{2},'double'),isa(P{3},'double'),isa(P{4},'double')]), error(message('images:deconvblind:psfCellElementsMustBeDouble')) end end; % Second, Find out if we have a function to put additional constraints on the PSF % function_classes = {'inline','function_handle','char'}; idx = []; for n = 3:nargin, idx = strmatch(class(varargin{n}),function_classes); if ~isempty(idx), [FunFcn,msgStruct] = fcnchk(varargin{n}); %only works on char, making it inline if ~isempty(msgStruct) error(msgStruct) end FunArg = [{0},varargin(n+1:nargin)]; try % how this function works, just in case. feval(FunFcn,FunArg{:}); catch ME Ftype = {'inline object','function_handle','expression ==>'}; Ffcnstr = {' ',' ',varargin{n}}; error(message('images:deconvblind:userSuppliedFcnFailed', Ftype{ idx }, Ffcnstr{ idx }, ME.message)) end funnum = n; break end end if isempty(idx), FunFcn = FunFcn_d; FunArg = FunArg_d; funnum = funnum_d; end % % Third, Assign the inputs for general deconvolution: % if funnum>7 error(message('images:validate:tooManyInputs',mfilename)); end switch funnum, case 4,% deconvblind(I,PSF,NUMIT,fun,...) NUMIT = varargin{3}; case 5,% deconvblind(I,PSF,NUMIT,DAMPAR,fun,...) NUMIT = varargin{3}; DAMPAR = varargin{4}; case 6,% deconvblind(I,PSF,NUMIT,DAMPAR,WEIGHT,fun,...) NUMIT = varargin{3}; DAMPAR = varargin{4}; WEIGHT = varargin{5}; case 7,% deconvblind(I,PSF,NUMIT,DAMPAR,WEIGHT,READOUT,fun,...) NUMIT = varargin{3}; DAMPAR = varargin{4}; WEIGHT = varargin{5}; READOUT = varargin{6}; end % Forth, Check validity of the gen.conv. input parameters: % % NUMIT check number of iterations if isempty(NUMIT), NUMIT = NUMIT_d; else %verify validity validateattributes(NUMIT,{'double'},... {'scalar' 'positive' 'integer' 'finite'},... mfilename,'NUMIT',3); end % DAMPAR check damping parameter if isempty(DAMPAR), DAMPAR = DAMPAR_d; elseif (numel(DAMPAR)~=1) && ~isequal(size(DAMPAR),sizeI), error(message('images:deconvblind:damparMustBeSizeOfInputImage')); elseif ~isa(DAMPAR,classI{2}), error(message('images:deconvblind:damparMustBeSameClassAsInputImage')); elseif ~strcmp(classI{2},'double'), DAMPAR = im2double(DAMPAR); end if ~isfinite(DAMPAR), error(message('images:deconvblind:damparMustBeFinite')); end % WEIGHT check weighting if isempty(WEIGHT), WEIGHT = ones(sizeI); else numw = numel(WEIGHT); validateattributes(WEIGHT,{'double'},{'finite'},mfilename,'WEIGHT',5); if (numw ~= 1) && ~isequal(size(WEIGHT),sizeI), error(message('images:deconvblind:weightMustBeSizeOfInputImage')); elseif numw == 1, WEIGHT = repmat(WEIGHT,sizeI); end; end % READOUT check read-out noise if isempty(READOUT), READOUT = READOUT_d; elseif (numel(READOUT)~=1) && ~isequal(size(READOUT),sizeI), error(message('images:deconvblind:readoutMustBeSizeOfInputImage')); elseif ~isa(READOUT,classI{2}), error(message('images:deconvblind:readoutMustBeSameClassAsInputImage')); elseif ~strcmp(classI{2},'double'), READOUT = im2double(READOUT); end if ~isfinite(READOUT), error(message('images:deconvblind:readoutMustBeFinite')); end;

PIL库 image相关wen'ti

``` import numpy as np from PIL import Image #此出在array函数里放入的参数不应该是列表或者元组吗？经过打印，发现Image.open的返回值，并不是，为什么可以直接丢入作为参数呢？ a=np.array(Image.open(r"C:\Users\21977\Desktop\QQ.jpg")) print(a) #a与[255,255,255]的维度都不一致，请问是怎样减得呢？ a=[255,255,255]-a im=Image.fromarray(a.astype("uint8")) im.save(r"C:\Users\21977\Desktop\newQQ.jpg") ```

QQ钱包接入问题，一直签名错误

我的网站最近准备接入QQ钱包支付，一切就绪但是接口一直报签名错误，我把所有请求字符串、签名和XML输出来了也并未发现哪里有问题。我想问问有没有哪位大哥有验签工具的，给我一份或者帮我看看这个有没有错误。 我开始以为是接口秘钥错误，然后重置了好几次，确定key并没有错误，后来又查看是不是缺了某个参数，后来检查了下也并没有。 按键名拼接的字符串并拼接秘钥key后的字符串： ``` body=121545445444&fee_type=CNY&mch_id=1508061891&nonce_str=1qva73hnb3q3ui5mz8d6fmbsf1b7og6v&notify_url=http://127.0.0.1:8088/pay/qqpay_notify.php&out_trade_no=202001081240075600250253&spbill_create_ip=127.0.0.1&total_fee=1000&trade_type=NATIVE&key=mQiw2feH4xqyd98q0B9ALdFxbxISo7iM ``` MD5后的值 ``` 3dfd38cdc138f4c2f80d992660f2c922 ``` 请求XML ``` <xml> <out_trade_no>202001081240075600250253</out_trade_no> <body>121545445444</body> <fee_type><![CDATA[CNY]]></fee_type> <notify_url><![CDATA[http://127.0.0.1:8088/pay/qqpay_notify.php]]></notify_url> <spbill_create_ip><![CDATA[127.0.0.1]]></spbill_create_ip> <total_fee>1000</total_fee> <trade_type><![CDATA[NATIVE]]></trade_type> <mch_id>1508061891</mch_id> <nonce_str><![CDATA[1qva73hnb3q3ui5mz8d6fmbsf1b7og6v]]></nonce_str> <sign><![CDATA[3DFD38CDC138F4C2F80D992660F2C922]]></sign> </xml> ``` 至于域名，我已经上传服务器在QQ钱包审核通过的域名下进行过测试，结果与本地测试相同，都是报SIGNERROR

初学MATLAB，求各位老师解释下这段代码，及参数的含义

function pushbutton3_Callback(hObject, eventdata, handles) global tu d=10; n=2; im=double(tu); [r,c,td]=size(im); fr=im(:,:,1); fg=im(:,:,2); fb=im(:,:,3); aftr=homofil(fr,d,r,c,n); aftg=homofil(fg,d,r,c,n); aftb=homofil(fb,d,r,c,n); zztx=cat(3,aftr,aftg,aftb); axes(handles.axes3); imshow(zztx); title('同态滤波增强效果图'); function im_eu=homofil(im,d,r,c,n) %巴特沃思高通滤波器（BHPF） A=zeros(r,c); for i=1:r for j=1:c A(i,j)=(((i-r/2).^2+(j-c/2).^2)).^(.5); H(i,j)=1/(1+((d/A(i,j))^(2*n))); end end %设置参数高频和低频值 alphaL=0.0999; alphaH=2.555; H=((alphaH-alphaL).*H)+alphaL; H=1-H; im_l=log2(1+im); im_f=fft2(im_l); im_nf=H.*im_f; im_n=abs(ifft2(im_nf)); im_e=exp(im_n); immin=min(min(im_e)); immax=max(max(im_e)); im_eu=uint8((im_e-immin)*255/(immax-immin));

MATLAB运行程序显示未定义函数或变量 'net'。

我的环境是MATLAB 2016a+vs2015+GPUwindows，想运行一段行人再识别的train源代码，代码是从github上下载的，但是总：显示未定义函数或变量 'net'。如下图所示、  源代码网址https://github.com/layumi/2016_person_re-ID.git 想请教一下这个要怎么处理，万分感谢。下面是我的代码 function train_id_net_res_2stream(varargin) % ------------------------------------------------------------------------- % Part 4.1: prepare the data % ------------------------------------------------------------------------- % Load character dataset imdb = load('./url_data.mat') ; imdb = imdb.imdb; % ------------------------------------------------------------------------- % Part 4.2: initialize a CNN architecture % ------------------------------------------------------------------------- net = resnet52_2stream(); net.params(net.getParamIndex('fc751f')).learningRate = 0.01; net.params(net.getParamIndex('fc751b')).learningRate = 0.2; net.conserveMemory = true; net.meta.normalization.averageImage = reshape([105.6920,99.1345,97.9152],1,1,3); % ------------------------------------------------------------------------- % Part 4.3: train and evaluate the CNN % ------------------------------------------------------------------------- opts.train.averageImage = net.meta.normalization.averageImage; opts.train.batchSize = 48; opts.train.continue = true; opts.train.gpus = 1; %Select gpu card. The gpu id in Matlab start from 1. opts.train.prefetch = false ; opts.train.expDir = './data/resnet52_2stream_drop0.9_new' ; % your model will store here opts.train.learningRate = [0.1*ones(1,70),0.01*ones(1,5)] ; opts.train.derOutputs = {'objective', 0.5,'objective_2', 0.5,'objective_final', 1} ; opts.train.weightDecay = 0.0005; opts.train.numEpochs = numel(opts.train.learningRate) ; [opts, ~] = vl_argparse(opts.train, varargin) ; % Call training function in MatConvNet [~,~] = cnn_train_dag(net, imdb, @getBatch,opts) ; % -------------------------------------------------------------------- function inputs = getBatch(imdb, batch,opts) % -------------------------------------------------------------------- im1_url = imdb.images.data(batch) ; label1 = imdb.images.label(:,batch) ; batchsize = numel(batch); % every epoch we will add negative pairs until 1:4 dividor = 2; dividor = min(5,dividor*power(1.01,opts.epoch)); half = round(batchsize/dividor); label_f = cat(1,ones(half,1,'single'),ones(batchsize-half,1,'single')*2); % select half from same class, second half from different class; batch2 = zeros(batchsize,1); for i=1:batchsize if(i<=half) batch2(i) = rand_same_class(imdb, batch(i)); else batch2(i) = rand_diff_class(imdb, batch(i)); end end im2_url = imdb.images.data(batch2) ; im1 = vl_imreadjpeg(im1_url,'Flip'); im2 = vl_imreadjpeg(im2_url,'Flip'); label2 = imdb.images.label(:,batch2) ; %------------------------------process data oim1 = zeros(224,224,3,batchsize,'single'); oim2 = zeros(224,224,3,batchsize,'single'); for i=1:batchsize x1 = randi(33);x2 = randi(33); y1 = randi(33);y2 = randi(33); tim1 = im1{i}; tim2 = im2{i}; temp1 = tim1(x1:x1+223,y1:y1+223,:); temp2 = tim2(x2:x2+223,y2:y2+223,:); oim1(:,:,:,i) = temp1; oim2(:,:,:,i) = temp2; end oim1 = bsxfun(@minus,oim1,opts.averageImage); oim2 = bsxfun(@minus,oim2,opts.averageImage); inputs = {'data',gpuArray(oim1),'data_2',gpuArray(oim2),'label',label1,'label_2',label2,'label_f',label_f}; ``` ```

如何将客户端（ubuntu18.04 nxlog+graylog-collector-sidecar）日志传给服务器端（centOS7 graylog2.0+mongo+elasticesearch）

已经完成的工作 1、服务器端graylog的web已经可以访问了 2、客户端graylog-collector-sidecar也安装配置完了，启动正常 3、修改了配置文件/etc/graylog/collector-sidecar/collector_sidecar.yml 如下： ``` server_url: http://192.168.1.7:12900 #graylog端的IP node_id: graylog-collector-sidecar collector_id: file:/etc/graylog/collector-sidecar/collector-id tags: - linux - apache - redis update_interval: 10 log_path: /var/log/graylog/collector-sidecar backends: - name: nxlog enabled: true binary_path: /usr/bin/nxlog configuration_path: /etc/graylog/collector-sidecar/generated/nxlog.conf ``` 4、nxlog安装好了，修改配置文件/etc/nxlog/nxlog.conf 如下，启动失败 ``` ######################################## # Modules # ######################################## #<Extension _syslog> # Module xm_syslog #</Extension> <Input in1> Module im_file File "/var/tmp/opencanary.log" #实际想要收集的log文件 SavePos TRUE </Input> #<Input in2> # Module im_tcp # Port 514 #</Input> <Output fileout1> Module om_udp Host 192.168.1.7 Port 12201 #与graylog的input端口对应 </Output> #<Output fileout2> # Module om_file # File "/var/log/nxlog/logmsg2.txt" #</Output> ######################################## # Routes # ######################################## <Route 1> Path in1 => fileout1 </Route> #<Route tcproute> # Path in2 => fileout2 #</Route> ``` 5、运行systemctl start nxlog.service报错，  问题： 怎么才能让graylog收集到日志，请教各位大佬。

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