openCV_python自带的ANN进行手写字体识别，报错。求助

 我用python3.6按照《OpenCV3计算机视觉》书上代码进行手写字识别，识别率很低，运行时还报了错:OpenCV(3.4.1) Error: Assertion failed ((type == 5 || type == 6) && inputs.cols == layer_sizes[0]) in cv::ml::ANN_MLPImpl::predict, file C:\projects\opencv-python\opencv\modules\ml\src\ann_mlp.cpp, line 411 ``` 具体代码如下：求大佬指点下 import cv2 import numpy as np import digits_ann as ANN def inside(r1, r2): x1, y1, w1, h1 = r1 x2, y2, w2, h2 = r2 if (x1 > x2) and (y1 > y2) and (x1 + w1 < x2 + w2) and (y1 + h1 < y2 + h2): return True else: return False def wrap_digit(rect): x, y, w, h = rect padding = 5 hcenter = x + w / 2 vcenter = y + h / 2 if (h > w): w = h x = hcenter - (w / 2) else: h = w y = vcenter - (h / 2) return (int(x - padding), int(y - padding), int(w + padding), int(h + padding)) ''' 注意：首次测试时，建议将使用完整的训练数据集，且进行多次迭代，直到收敛 如：ann, test_data = ANN.train(ANN.create_ANN(100), 50000, 30) ''' ann, test_data = ANN.train(ANN.create_ANN(10), 50000, 1) # 调用所需识别的图片，并处理 path = "C:\\Users\\64601\\PycharmProjects\Ann\\images\\numbers.jpg" img = cv2.imread(path, cv2.IMREAD_UNCHANGED) bw = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) bw = cv2.GaussianBlur(bw, (7, 7), 0) ret, thbw = cv2.threshold(bw, 127, 255, cv2.THRESH_BINARY_INV) thbw = cv2.erode(thbw, np.ones((2, 2), np.uint8), iterations=2) image, cntrs, hier = cv2.findContours(thbw.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) rectangles = [] for c in cntrs: r = x, y, w, h = cv2.boundingRect(c) a = cv2.contourArea(c) b = (img.shape[0] - 3) * (img.shape[1] - 3) is_inside = False for q in rectangles: if inside(r, q): is_inside = True break if not is_inside: if not a == b: rectangles.append(r) for r in rectangles: x, y, w, h = wrap_digit(r) cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) roi = thbw[y:y + h, x:x + w] try: digit_class = ANN.predict(ann, roi)[0] except: print("except") continue cv2.putText(img, "%d" % digit_class, (x, y - 1), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0)) cv2.imshow("thbw", thbw) cv2.imshow("contours", img) cv2.waitKey() cv2.destroyAllWindows() ####### import cv2 import pickle import numpy as np import gzip """OpenCV ANN Handwritten digit recognition example Wraps OpenCV's own ANN by automating the loading of data and supplying default paramters, such as 20 hidden layers, 10000 samples and 1 training epoch. The load data code is taken from http://neuralnetworksanddeeplearning.com/chap1.html by Michael Nielsen """ def vectorized_result(j): e = np.zeros((10, 1)) e[j] = 1.0 return e def load_data(): with gzip.open('C:\\Users\\64601\\PycharmProjects\\Ann\\mnist.pkl.gz') as fp: # 注意版本不同，需要添加传入第二个参数encoding='bytes'，否则出现编码错误 training_data, valid_data, test_data = pickle.load(fp, encoding='bytes') fp.close() return (training_data, valid_data, test_data) def wrap_data(): # tr_d数组长度为50000，va_d数组长度为10000，te_d数组长度为10000 tr_d, va_d, te_d = load_data() # 训练数据集 training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]] training_results = [vectorized_result(y) for y in tr_d[1]] training_data = list(zip(training_inputs, training_results)) # 校验数据集 validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]] validation_data = list(zip(validation_inputs, va_d[1])) # 测试数据集 test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]] test_data = list(zip(test_inputs, te_d[1])) return (training_data, validation_data, test_data) def create_ANN(hidden=20): ann = cv2.ml.ANN_MLP_create() # 建立模型 ann.setTrainMethod(cv2.ml.ANN_MLP_RPROP | cv2.ml.ANN_MLP_UPDATE_WEIGHTS) # 设置训练方式为反向传播 ann.setActivationFunction( cv2.ml.ANN_MLP_SIGMOID_SYM) # 设置激活函数为SIGMOID，还有cv2.ml.ANN_MLP_IDENTITY,cv2.ml.ANNMLP_GAUSSIAN ann.setLayerSizes(np.array([784, hidden, 10])) # 设置层数，输入784层，输出层10 ann.setTermCriteria((cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 100, 0.1)) # 设置终止条件 return ann def train(ann, samples=10000, epochs=1): # tr:训练数据集; val:校验数据集; test:测试数据集; tr, val, test = wrap_data() for x in range(epochs): counter = 0 for img in tr: if (counter > samples): break if (counter % 1000 == 0): print("Epoch %d: Trained %d/%d" % (x, counter, samples)) counter += 1 data, digit = img ann.train(np.array([data.ravel()], dtype=np.float32), cv2.ml.ROW_SAMPLE, np.array([digit.ravel()], dtype=np.float32)) print("Epoch %d complete" % x) return ann, test def predict(ann, sample): resized = sample.copy() rows, cols = resized.shape if rows != 28 and cols != 28 and rows * cols > 0: resized = cv2.resize(resized, (28, 28), interpolation=cv2.INTER_CUBIC) return ann.predict(np.array([resized.ravel()], dtype=np.float32)) ```

spark运行scala的jar包

   有人遇到过类似的问题吗？ 我的尝试： 当没Master节点的Worker进程，运行会报错，当开启了Master节点的Worker进程有时不会报错，但是会说内存不够，但是我觉得不是这个问题，也能得出一定的结果，但并不是预期的结果。 执行的命令：bin/spark-submit --master spark://node1:7077 --class cn.itcast.WordCount_Online --executor-memory 1g --total-executor-cores 1 ~/data/spark_chapter02-1.0-SNAPSHOT.jar /spark/test/words.txt /spark/test/out jar包是在idea中打包的，用的是scala语言，主要作用是词频统计 scala代码： ``` package cn.itcast import org.apache.spark.rdd.RDD import org.apache.spark.{SparkConf, SparkContext} object WordCount_Online { def main(args: Array[String]):Unit={ val sparkConf = new SparkConf().setAppName("WordCount_Online") val sparkContext = new SparkContext(sparkConf) val data : RDD[String] = sparkContext.textFile(args(0)) val words :RDD[String] = data.flatMap(_.split(" ")) val wordAndOne :RDD[(String,Int)] = words.map(x => (x,1)) val result :RDD[(String,Int)] = wordAndOne.reduceByKey(_+_) result.saveAsTextFile(args(1)) sparkContext.stop() } } ``` 我也做了很多尝试，希望懂的人可以交流一下

TensorFlow报错：Shape (44, ?) must have rank at least 3 ？

用TensorFlow执行RNN，报错ValueError: Shape (44, ?) must have rank at least 3，下面是程序的部分代码，请问应该在哪里修改下？谢谢 解析函数： ``` feature = ['feature1',......,'feature44'] label = 'label2' featureNames = list(feature) featureNames.append(label) columns = [tf.FixedLenFeature(shape=[1], dtype=tf.float32) for k in featureNames] featuresDict = dict(zip(featureNames, columns)) def parse_tfrecord(example_proto): parsed_features = tf.parse_single_example(example_proto, featuresDict) labels = parsed_features.pop(label) return parsed_features, tf.cast(labels, tf.int32) ``` 输入函数（原始数据是有44个特征值的数值序列，每个序列为一样本）： ``` def tfrecord_input_fn(fileName,numEpochs=None,shuffle=True,batchSize=None): #读取tfrecord数据 dataset = tf.data.TFRecordDataset(fileName, compression_type='GZIP') #执行解析函数 dataset = dataset.map(parse_tfrecord) #打乱数据 if shuffle: dataset = dataset.shuffle(buffer_size=batchSize * 100*numEpochs) #每32个样本作为一个batch dataset = dataset.batch(32) #重复数据 dataset = dataset.repeat(numEpochs) print('features:',features) print('labels:',labels) iterator = dataset.make_one_shot_iterator() features, labels = iterator.get_next() return features, labels ``` 打印返回值结果： ``` features: {'feature1': <tf.Tensor 'IteratorGetNext_21:0' shape=(?, 1) dtype=float32>, 'feature2': <tf.Tensor 'IteratorGetNext_21:1' shape=(?, 1) dtype=float32>,......， 'feature44': <tf.Tensor 'IteratorGetNext_21:43' shape=(?, 1) dtype=float32>} labels: Tensor("IteratorGetNext_21:44", shape=(?, 1), dtype=int32) ``` 执行网络后报错： ``` ValueError: Shape (44, ?) must have rank at least 3 ```

cuda 报错 CUDA_ERROR_LAUNCH_FAILED

template<typename T> __global__ void pooling_max_kernel (T* pooled, const T* data, const T* stochastic_value, const int mode, const int pooledWidth, const int pooledHeight, const int pooledVolume, const int width, const int height, const int poolWidth, const int poolHeight, const int strideX, const int strideY, const int padLeft, const int padTop) { int pooledIndex = threadIdx.x + blockIdx.x * blockDim.x; int stochastic_value_index=0; if (pooledIndex < pooledVolume) { int px = pooledIndex ; int py = px / pooledWidth ; int pz = py / pooledHeight ; px %= pooledWidth ; py %= pooledHeight ; data += pz * (width*height) ; int x1 = px * strideX - padLeft ; int y1 = py * strideY - padTop ; int x2 = min(x1 + poolWidth, width) ; int y2 = min(y1 + poolHeight, height) ; x1 = max(x1, 0) ; y1 = max(y1, 0) ; //T *savedata;T *dataprob; //cudaMalloc((void**)&savedata,sizeof(T)*9);cudaMalloc((void**)&dataprob,sizeof(T)*9);/////////////////////////////////////////////////定义变量分配内存 T savedata[9]={0};T dataprob[10]={0};dataprob[0]=0; T sumdata=0;T weightsum=0;int i=0;/////////////////////////////lyz //T bestValue = data[y1 * width + x1] ; for (int y = y1 ; y < y2 ; ++y) { for (int x = x1 ; x < x2 ; ++x) { //bestValue = max(bestValue, data[y * width + x]) ; savedata[i]=data[y * width + x]; sumdata=sumdata+data[y * width + x]; i=i+1; if (i>8) {i=0;} } } if(mode==0) { T randdata = stochastic_value[pooledIndex]; if (sumdata>0) { for(int j=0;j<poolWidth*poolHeight;++j) { dataprob[j+1]=dataprob[j]+savedata[j]/sumdata; if (randdata<dataprob[j+1]) { stochastic_value_index=j; break; } } pooled[pooledIndex] = savedata[stochastic_value_index] ; } else { pooled[pooledIndex] = 0 ; } } else { if (sumdata>0) { for(int j=0;j<poolWidth*poolHeight;++j) { weightsum=weightsum+savedata[j]*savedata[j]/sumdata; } pooled[pooledIndex] = weightsum ; } else { pooled[pooledIndex] = 0 ; } } //cudaFree(savedata);cudaFree(dataprob); } } 另，在kernel函数中定义变量需要分配内存吗，我之前用注释掉的那个分配内存，编译的时候回报错

tensorflow训练网络报错Invalid argument

##1.问题 程序报错，提示：Invalid argument: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,24] ##2.代码 ``` import time import numpy as np import pandas as pd import tensorflow as tf import matplotlib.pyplot as plt # import dataset input_Dir = 'E:/data/input_H.csv' output_Dir = 'E:/data/output_H.csv' x_data = pd.read_csv(input_Dir, header = None) y_data = pd.read_csv(output_Dir, header = None) x_data = x_data.values y_data = y_data.values x_data = x_data.astype('float32') y_data = y_data.astype('float32') print("DATASET READY") # from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2, random_state=1) row, column = x_train.shape row = float(row) # define structure of neural network n_hidden_1 = 250 n_hidden_2 = 128 n_input = 250 n_classes = 24 #initialize parameters x = tf.placeholder(tf.float32, [None, n_input]) y = tf.placeholder(tf.float32, [None, n_classes]) keep_prob = tf.placeholder(tf.float32) stddev = 0.1 weights = { 'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=stddev)), 'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=stddev)), 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], stddev=stddev)) } biases = { 'b1': tf.Variable(tf.random_normal([n_hidden_1], stddev=stddev)), 'b2': tf.Variable(tf.random_normal([n_hidden_2], stddev=stddev)), 'out': tf.Variable(tf.random_normal([n_classes], stddev=stddev)) } print("NETWORK READY") # forward propagation def multilayer_perceptron(_X, _weights, _biases): layer_1 = tf.nn.leaky_relu(tf.add(tf.matmul(_X, _weights['w1']), _biases['b1'])) layer_2 = tf.nn.leaky_relu(tf.add(tf.matmul(layer_1, _weights['w2']), _biases['b2'])) return (tf.add(tf.matmul(layer_2, _weights['out']), _biases['out'])) # pred = multilayer_perceptron(x, weights, biases) cost = tf.reduce_mean(tf.square(y - pred)) optm = tf.train.GradientDescentOptimizer(learning_rate=0.03).minimize(cost) init = tf.global_variables_initializer() print("FUNCTIONS READY") n_epochs = 100000 batch_size = 512 n_batches = np.int(np.ceil(row / batch_size)) def fetch_batch(epoch, batch_index, batch_size): # 随机获取小批量数据 np.random.seed(epoch * n_batches + batch_index) indices = np.random.randint(row, size = batch_size) return x_train[indices], y_train[indices] iter = 10000 sess = tf.Session() sess.run(tf.global_variables_initializer()) feeds_test = {x: x_test, y: y_test, keep_prob: 1} for epoch in range(n_epochs): # 总共循环次数 for batch_index in range(n_batches): x_batch, y_batch = fetch_batch(epoch, batch_index, batch_size) feeds_train = {x: x_batch, y: y_batch, keep_prob: 1} sess.run(optm, feed_dict=feeds_train) print("EPOCH %d HAS FINISHED" % (epoch)) print("COST %d :" % (epoch)) print(sess.run(cost),feed_dict=feeds_train) print("\n") sess.close() print("FINISHED") ``` ##3.报错信息 Traceback (most recent call last): File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1356, in _do_call return fn(*args) File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1341, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1429, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found. (0) Invalid argument: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,24] [[{{node Placeholder_1}}]] [[Mean/_7]] (1) Invalid argument: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,24] [[{{node Placeholder_1}}]] 0 successful operations. 0 derived errors ignored. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\IPython\core\interactiveshell.py", line 3296, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-762bc58e4306>", line 1, in <module> runfile('C:/Users/Administrator/Desktop/main/demo3.py', wdir='C:/Users/Administrator/Desktop/main') File "E:\Program Files\PyCharm 2019.1.3\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile #求问问题出在什么地方？

我在用facenet模型中的classifier.py文件中训练自己的数据集的时候配置了相关参数所在的路径，但是却发生了如下报错，请问是什么原因？

作为一个刚入门的小白在这方面的学习还有许多的不懂，我在用facenet模型中的classifier.py文件中训练自己的数据集的时候配置了相关参数所在的路径，但是却发生了如下报错，请问是什么原因？ ``` def parse_arguments(argv): parser = argparse.ArgumentParser() parser.add_argument('mode', type=str, choices=['TRAIN', 'CLASSIFY'], help='Indicates if a new classifier should be trained or a classification ' + 'model should be used for classification', default='TRAIN')#训练模型 parser.add_argument('data_dir', type=str, help='Path to the data directory containing aligned LFW face patches.',default='D:/pyCharm/facenet/data_dir/African_convert')#添加数据集文件夹 parser.add_argument('model', type=str, help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file',default='D:/pyCharm/facenet/20170512-110547') parser.add_argument('classifier_filename', help='Classifier model file name as a pickle (.pkl) file. ' + 'For training this is the output and for classification this is an input.' ,default='pick/classifier.pkl') parser.add_argument('--use_split_dataset', help='Indicates that the dataset specified by data_dir should be split into a training and test set. ' + 'Otherwise a separate test set can be specified using the test_data_dir option.', action='store_true') parser.add_argument('--test_data_dir', type=str, help='Path to the test data directory containing aligned images used for testing.') parser.add_argument('--batch_size', type=int, help='Number of images to process in a batch.', default=90) parser.add_argument('--image_size', type=int, help='Image size (height, width) in pixels.', default=160) parser.add_argument('--seed', type=int, help='Random seed.', default=666) parser.add_argument('--min_nrof_images_per_class', type=int, help='Only include classes with at least this number of images in the dataset', default=20) parser.add_argument('--nrof_train_images_per_class', type=int, help='Use this number of images from each class for training and the rest for testing', default=10) return parser.parse_args(argv) if __name__ == '__main__': main(parse_arguments(sys.argv[1:])) ``` 报错信息如下： usage: classifier.py [-h] [--use_split_dataset] [--test_data_dir TEST_DATA_DIR] [--batch_size BATCH_SIZE] [--image_size IMAGE_SIZE] [--seed SEED] [--min_nrof_images_per_class MIN_NROF_IMAGES_PER_CLASS] [--nrof_train_images_per_class NROF_TRAIN_IMAGES_PER_CLASS] {TRAIN,CLASSIFY} data_dir model classifier_filename classifier.py: error: the following arguments are required: mode, data_dir, model, classifier_filename   谢谢！！！

python报错：KeyError: 'user_id'

#coding:utf-8 import pandas as pd import numpy as np import time import datetime import gc from sklearn.preprocessing import LabelEncoder,OneHotEncoder def pre_process(data): cols = data.columns.tolist() keys = ['instance_id', 'day'] for k in keys: cols.remove(k) return data, cols def dorollWin(data): data['context_timestamp_str'] = data['context_timestamp'].astype(str) user_time_join = data.groupby(test.user_id)['context_timestamp_str'].agg(lambda x:';'.join(x)).reset_index() user_time_join.rename(columns={'context_timestamp_str':'user_time_join'},inplace = True) data = pd.merge(data,user_time_join,on=[test.user_id],how='left') user_shop_time_join = data.groupby([test.user_id,'shop_id'])['context_timestamp_str'].agg(lambda x:';'.join(x)).reset_index() user_shop_time_join.rename(columns={'context_timestamp_str':'user_shop_time_join'},inplace = True) data = pd.merge(data,user_shop_time_join,on=[test.user_id,'shop_id'],how='left') user_item_time_join = data.groupby([test.user_id,'item_id'])['context_timestamp_str'].agg(lambda x:';'.join(x)).reset_index() user_item_time_join.rename(columns={'context_timestamp_str':'user_item_time_join'},inplace = True) data = pd.merge(data,user_item_time_join,on=[test.user_id,'item_id'],how='left') data['index_']=data.index del user_time_join,user_shop_time_join,user_item_time_join nowtime=data.context_timestamp.values user_time=data.user_time_join.values user_shop_time=data.user_shop_time_join.values user_item_time=data.user_item_time_join.values data_len=data.shape[0] user_time_10_bf=np.zeros(data_len) user_time_10_af=np.zeros(data_len) user_shop_time_10_bf=np.zeros(data_len) user_shop_time_10_af=np.zeros(data_len) user_item_time_10_bf=np.zeros(data_len) user_item_time_10_af=np.zeros(data_len) a=time.time() for i in range(data_len): df1=nowtime[i] df2=user_time[i].split(';') df2_len=len(df2) for j in range(df2_len): if ((int(df2[j])-df1)<600) & ((int(df2[j])-df1)>0): user_time_10_bf[i]+=1 if ((int(df2[j])-df1)>-600) & ((int(df2[j])-df1)<0): user_time_10_af[i]+=1 df3=user_shop_time[i].split(';') df3_len=len(df3) for j in range(df3_len): if ((int(df3[j])-df1)<600) & ((int(df3[j])-df1)>0): user_shop_time_10_bf[i]+=1 if ((int(df3[j])-df1)>-600) & ((int(df3[j])-df1)<0): user_shop_time_10_af[i]+=1 df4=user_item_time[i].split(';') df4_len=len(df4) for j in range(df4_len): if ((int(df4[j])-df1)<600) & ((int(df4[j])-df1)>0): user_item_time_10_bf[i]+=1 if ((int(df4[j])-df1)>-600) & ((int(df4[j])-df1)<0): user_item_time_10_af[i]+=1 print(time.time()-a) data['user_count_10_bf']=user_time_10_bf data['user_count_10_af']=user_time_10_af data['user_shop_count_10_bf']=user_shop_time_10_bf data['user_shop_count_10_af']=user_shop_time_10_af data['user_item_count_10_bf']=user_item_time_10_bf data['user_item_count_10_af']=user_item_time_10_af drops = ['context_timestamp_str', 'user_time_join', 'user_shop_time_join', 'user_item_time_join', 'index_'] data = data.drop(drops, axis=1) return data def doSize(data): add = pd.DataFrame(data.groupby(["shop_id", "day"]).item_id.nunique()).reset_index() add.columns = ["shop_id", "day", "shop_item_unique_day"] data = data.merge(add, on=["shop_id", "day"], how="left") user_query_day = data.groupby(['user_id', 'day']).size().reset_index().rename(columns={0: 'user_id_query_day'}) data = pd.merge(data, user_query_day, how='left', on=['user_id', 'day']) data['min_10'] = data['minute'] // 10 data['min_15'] = data['minute'] // 15 data['min_30'] = data['minute'] // 30 data['min_45'] = data['minute'] // 45 # user 不同时间段点击次数 min10_user_click = data.groupby(['user_id', 'day', 'hour', 'min_10']).size().reset_index().rename(columns={0:'min10_user_click'}) min15_user_click = data.groupby(['user_id', 'day', 'hour', 'min_15']).size().reset_index().rename(columns={0:'min15_user_click'}) min30_user_click = data.groupby(['user_id', 'day', 'hour', 'min_30']).size().reset_index().rename(columns={0:'min30_user_click'}) min45_user_click = data.groupby(['user_id', 'day', 'hour', 'min_45']).size().reset_index().rename(columns={0:'min45_user_click'}) data = pd.merge(data, min10_user_click, 'left', on=['user_id', 'day', 'hour', 'min_10']) data = pd.merge(data, min15_user_click, 'left', on=['user_id', 'day', 'hour', 'min_15']) data = pd.merge(data, min30_user_click, 'left', on=['user_id', 'day', 'hour', 'min_30']) data = pd.merge(data, min45_user_click, 'left', on=['user_id', 'day', 'hour', 'min_45']) del data['min_10'] del data['min_15'] del data['min_30'] del data['min_45'] return data def doElse(data): pass def main(): path = 'F:/18ijcaidata/' train = pd.read_csv(path+'train_day7.csv',encoding='utf-8') test = pd.read_csv(path+'test_day7.csv',encoding='utf-8') data = pd.concat([train, test]) print('初始维度:', data.shape) data, cols = pre_process(data) print('pre_process:', data.shape) ################################## data = dorollWin(data) print('dorollWin:', data.shape) data = doSize(data) print('doSize:', data.shape) ################################## data = data.drop(cols, axis=1) # 得到7号训练集 data = data.loc[data.day == 7] data = data.drop('day', axis=1) print('经过处理后,7号数据集最终维度::',data.shape) print(data.columns.tolist()) data.to_csv(path+'103_statistics_feat.csv', index=False) if __name__ == '__main__': #??????????????运行此行，出现key error:'user_id' main() ``` ```

数据库导入sql报错1215

/* Navicat MySQL Data Transfer Source Server : localhost_3306 Source Server Version : 50521 Source Host : localhost:3306 Source Database : mybatis Target Server Type : MYSQL Target Server Version : 50521 File Encoding : 65001 Date: 2015-04-09 16:03:53 */ SET FOREIGN_KEY_CHECKS=0; -- ---------------------------- -- Table structure for `orders` -- ---------------------------- DROP TABLE IF EXISTS `orders`; CREATE TABLE `orders` ( `id` int(11) NOT NULL AUTO_INCREMENT, `user_id` int(11) NOT NULL COMMENT '下单用户id', `number` varchar(32) NOT NULL COMMENT '订单号', `createtime` datetime NOT NULL COMMENT '创建订单时间', `note` varchar(100) DEFAULT NULL COMMENT '备注', PRIMARY KEY (`id`), KEY `FK_orders_1` (`user_id`), CONSTRAINT `FK_orders_id` FOREIGN KEY (`user_id`) REFERENCES `user` (`id`) ON DELETE NO ACTION ON UPDATE NO ACTION ) ENGINE=InnoDB AUTO_INCREMENT=6 DEFAULT CHARSET=utf8; -- ---------------------------- -- Records of orders -- ---------------------------- INSERT INTO `orders` VALUES ('3', '1', '1000010', '2015-02-04 13:22:35', null); INSERT INTO `orders` VALUES ('4', '1', '1000011', '2015-02-03 13:22:41', null); INSERT INTO `orders` VALUES ('5', '10', '1000012', '2015-02-12 16:13:23', null); -- ---------------------------- -- Table structure for `user` -- ---------------------------- DROP TABLE IF EXISTS `user`; CREATE TABLE `user` ( `id` int(11) NOT NULL AUTO_INCREMENT, `username` varchar(32) NOT NULL COMMENT '用户名称', `birthday` date DEFAULT NULL COMMENT '生日', `sex` char(1) DEFAULT NULL COMMENT '性别', `address` varchar(256) DEFAULT NULL COMMENT '地址', PRIMARY KEY (`id`) ) ENGINE=InnoDB AUTO_INCREMENT=27 DEFAULT CHARSET=utf8; -- ---------------------------- -- Records of user -- ---------------------------- INSERT INTO `user` VALUES ('1', '王五', null, '2', null); INSERT INTO `user` VALUES ('10', '张三', '2014-07-10', '1', '北京市'); INSERT INTO `user` VALUES ('16', '张小明', null, '1', '河南郑州'); INSERT INTO `user` VALUES ('22', '陈小明', null, '1', '河南郑州'); INSERT INTO `user` VALUES ('24', '张三丰', null, '1', '河南郑州'); INSERT INTO `user` VALUES ('25', '陈小明', null, '1', '河南郑州'); INSERT INTO `user` VALUES ('26', '王五', null, null, null);

有全部源码，我调用openssl库的RSA和AES算法进行加密。加密字符串太长就会报错，具体如下。

报错信息是这样的： Error encrypting message: error:0406D06E:rsa routines:RSA_padding_add_PKCS1_type_2:data too large for key size Error decrypting message: error:0407106B:rsa routines:RSA_padding_check_PKCS1_type_2:block type is not 02 我用的Linux环境进行开发的，不过也应该没有太大关系啦！ 另外代码上有什么问题也可以指出来，万分感谢！！！ 具体代码 如下： ``` #include <openssl/rsa.h> #include <openssl/sha.h> #include <openssl/ssl.h> #include <openssl/aes.h> #include <openssl/evp.h> #include <openssl/pem.h> #include <openssl/err.h> #include <assert.h> #include <iostream> #include <string.h> #include <stdio.h> #include <string> #include <stdlib.h> #define RSA_KEYLEN 2048 #define AES_KEYLEN 128 #define AES_ROUNDS 6 #define KEY_SERVER_PRI 0 #define KEY_SERVER_PUB 1 #define KEY_CLIENT_PUB 2 #define KEY_AES 3 #define KEY_AES_IV 4 #define KEY_LENGTH 2048 #define PUB_EXP 3 //用AES进行加密 int EncodeAES(unsigned char* password,unsigned int pass_len, unsigned char* data,unsigned int data_len,unsigned char** out_data,unsigned int *out_len) { AES_KEY aes_key; if(AES_set_encrypt_key((const unsigned char*)password, 192, &aes_key) < 0) { assert(false); return -1; } std::string strRet; std::string data_bak((const char*)data); unsigned int data_length = data_len; int padding = 0; if (data_len % AES_BLOCK_SIZE > 0) { padding = AES_BLOCK_SIZE - data_len % AES_BLOCK_SIZE; } data_length += padding; while (padding > 0) { data_bak += '\0'; padding--; } for(unsigned int i = 0; i < data_length/AES_BLOCK_SIZE; i++) { std::string str16 = data_bak.substr(i*AES_BLOCK_SIZE, AES_BLOCK_SIZE); unsigned char out[AES_BLOCK_SIZE]; ::memset(out, 0, AES_BLOCK_SIZE); AES_encrypt((const unsigned char*)str16.c_str(), out, &aes_key); strRet += std::string((const char*)out, AES_BLOCK_SIZE); } *out_len=strRet.length(); *out_data=(unsigned char*)malloc(*out_len); *out_data=(unsigned char*)strRet.c_str(); return 0; } //用aes进行解密 int DecodeAES(unsigned char *password,unsigned int pass_len, unsigned char* data,unsigned int data_len,unsigned char** out_data,unsigned int *out_len ) { AES_KEY aes_key; if(AES_set_decrypt_key((const unsigned char*)password, 192, &aes_key) < 0) { assert(false); return -1; } std::string strRet; std::string strData((const char*)data); try { for(unsigned int i = 0; i < data_len/AES_BLOCK_SIZE; i++) { std::string str16 = strData.substr(i*AES_BLOCK_SIZE, AES_BLOCK_SIZE); unsigned char out[AES_BLOCK_SIZE]; ::memset(out, 0, AES_BLOCK_SIZE); AES_decrypt((const unsigned char*)str16.c_str(), out, &aes_key); strRet += std::string((const char*)out, AES_BLOCK_SIZE); } } catch (std::exception const &exc) { std::cerr << "Exception caught " << exc.what() << "\n"; } catch (...) { std::cerr << "Unknown exception caught\n"; } *out_len=strRet.length(); *out_data=(unsigned char*)malloc(*out_len); *out_data=(unsigned char*)strRet.c_str(); return 0; } //生成RSA需要的公钥和私钥 int generateRSAKeys(char ** pri_key, char ** pub_key) { RSA *keypair = RSA_generate_key(KEY_LENGTH, PUB_EXP, NULL, NULL); // To get the C-string PEM form: BIO *pri = BIO_new(BIO_s_mem()); BIO *pub = BIO_new(BIO_s_mem()); PEM_write_bio_RSAPrivateKey(pri, keypair, NULL, NULL, 0, NULL, NULL); PEM_write_bio_RSAPublicKey(pub, keypair); size_t pri_len = BIO_pending(pri); size_t pub_len = BIO_pending(pub); char* prikey = (char*)malloc(pri_len + 1); char* pubkey = (char*)malloc(pub_len + 1); if (prikey == NULL&&pubkey == NULL) { return -1; } BIO_read(pri, prikey, pri_len); BIO_read(pub, pubkey, pub_len); *pri_key = prikey; *pub_key = pubkey; RSA_free(keypair); BIO_free_all(pri); BIO_free_all(pub); return 0; } //用RSA公钥进行加密 int encryptWithPub(char *pub_key,char* msg, char **encrypt, int* encrypt_len ) { RSA *rsa = NULL; BIO* keybio = BIO_new_mem_buf((void *)pub_key, -1); char *err = (char*)malloc(130); if (keybio == NULL) { printf("failed to create key BIO\n"); return -1; } #ifdef RSAPUBKEY if ((rsa = PEM_read_bio_RSA_PUBKEY(keybio, NULL, NULL, NULL)) == NULL) #else if ((rsa = PEM_read_bio_RSAPublicKey(keybio, NULL, NULL, NULL)) == NULL) #endif if (!rsa){ printf(" rsa is null %s\n", ERR_error_string(ERR_get_error(), NULL)); return -1; } *encrypt_len = RSA_size(rsa); *encrypt = (char *)malloc(1024); if ((RSA_public_encrypt(strlen(msg) + 1, (unsigned char*)msg, (unsigned char*)*encrypt,rsa, RSA_PKCS1_PADDING)) == -1) { ERR_load_crypto_strings(); ERR_error_string(ERR_get_error(), err); fprintf(stderr, "Error encrypting message: %s\n", err); return -1; } RSA_free(rsa); free(err); BIO_free_all(keybio); // *encrypt_len=enstr.length(); return 0; } //用RSA私钥进行解密 int decryptWtihPri(char *pri_key,char* msg,char **decrypt, int encrypt_len){ RSA *rsa = NULL; BIO* keybio = BIO_new_mem_buf(pri_key, -1); if (keybio == NULL) { printf("failed to create key BIO\n"); return -1; } #ifdef RSAPRIVATE if ((rsa = PEM_read_bio_RSA_PRIVATE(keybio, NULL, NULL, NULL)) == NULL) #else if ((rsa = PEM_read_bio_RSAPrivateKey(keybio, NULL, NULL, NULL)) == NULL) #endif if (!rsa){ printf(" rsa null %s\n", ERR_error_string(ERR_get_error(), NULL)); return -1; } char *err = (char*)malloc(130); *decrypt = (char*)malloc(encrypt_len); if (RSA_private_decrypt(encrypt_len, (unsigned char*)msg, (unsigned char*)*decrypt, rsa, RSA_PKCS1_PADDING) == -1) { ERR_load_crypto_strings(); ERR_error_string(ERR_get_error(), err); fprintf(stderr, "Error decrypting message: %s\n", err); return -1; } RSA_free(rsa); free(err); BIO_free_all(keybio); return 0; } /** *测试函数部分 */ int test_aes(); int test_rsa(); int main() { test_rsa(); test_aes(); return 0; } int test_rsa() { printf("\n----------THE RSA TESTING -------- \n"); char * pri_key; char * pub_key; generateRSAKeys(&pri_key, &pub_key); //printf("\n%s\n%s\n", pri_key, pub_key); char *encrypt; char *decrypt; int encrypt_length; encryptWithPub(pub_key,(char *)"i havei have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a key a key",&encrypt, &encrypt_length); decryptWtihPri( pri_key,encrypt, &decrypt,encrypt_length); printf("decrypted message: %s\n", decrypt); return 0; } int test_aes() { printf("\n----------THE AES TESTING --------\n"); char* pass=(char*)"key"; int ret; char* data=(char*)"infocoei have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a key ei have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a key re big tree "; char *output=NULL; unsigned int out_len; char *newoutput=NULL; unsigned int new_len; std::cout<<"the length of key："<<strlen(pass)<<std::endl; std::cout<<"before encryption："<<data<<std::endl; ret=EncodeAES((unsigned char*)pass, strlen(pass),(unsigned char *)data,strlen(data),(unsigned char **)&output, &out_len); //std::cout<<"the en len："<<out_len<<std::endl; ret=DecodeAES((unsigned char*)pass, strlen(pass),(unsigned char *)output,out_len,(unsigned char **)&newoutput, &new_len); //std::cout<<"the deenc len："<<new_len<<std::endl; std::cout<<"after decryption："<<newoutput<<std::endl; } ``` 代码的运行结果如下： ``` ----------THE RSA TESTING -------- Error encrypting message: error:0406D06E:rsa routines:RSA_padding_add_PKCS1_type_2:data too large for key size Error decrypting message: error:0407106B:rsa routines:RSA_padding_check_PKCS1_type_2:block type is not 02 decrypted message: 惙l= ----------THE AES TESTING -------- the length of key：3 before encryption：infocoei have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a key ei have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a keyi have a key re big tree Exception caught basic_string::substr after decryption：Ϝż§§<¾yK*. [root@localhost boost_encryption]# ``` 我的邮箱是fubohuauser@163.com 有啥问题也可以一起联系交流。谢谢

data,label=read_img(path)报错

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新手请教下简单的mybatis问题

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数据库存储过程错误 求大佬来帮帮忙看看错误在哪里

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请求大佬支援!!!单链表报错

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WPF中Graphics g = this.CreateGraphics();报错

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我在做一个黑白棋游戏，想通过服务器转发指令实现客户端落子。在客户端接收数据然后转成int去判断落子的坐标然后报错了，然后服务器收到一堆空格。private void receiveData(object socket) { Socket ortherSocket = (Socket)clientSocket; while (true) { try { String data = Receive(ortherSocket); // 接收客户端发送的信息 if (!data.Equals("")) { s = data; s1 = int.Parse(data); if (s != "") { Sc(s1); } } } catch (Exception ex) { //ortherSocket.Shutdown(SocketShutdown.Both); ortherSocket.Close(); break; } } } ``` ``` 

帮帮忙吧，急哭了：python实现拉格朗日插值进行缺失值插补时报错，出现Keyerror：0问题？

 数据截图 ``` import pandas as pd from scipy.interpolate import lagrange inputfile = './test1.xlsx' outputfile = './test1_1.xlsx' def ployinterp_column(s,n,k=6): y = s[list(range(n-k,n)) + list(range(n+1,n+1-k))] y = y[y.notnull()] return lagrange(y.index,list(y))(n) for i in data.columns: for j in range(len(data)): if (data[i].isnull())[j]: data[i][j] = ployinterp_column(data[i],j) ``` 报错 ``` KeyError Traceback (most recent call last) <ipython-input-28-02cf873cddff> in <module>() 1 for i in data.columns: 2 for j in range(len(data)): ----> 3 if (data[i].isnull())[j]: 4 if (data[i].isnull())[j] in data: 5 data[i][j] = ployinterp_column(data[i],j) C:\Anaconda3\lib\site-packages\pandas\core\series.py in __getitem__(self, key) 765 key = com._apply_if_callable(key, self) 766 try: --> 767 result = self.index.get_value(self, key) 768 769 if not is_scalar(result): C:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_value(self, series, key) 3116 try: 3117 return self._engine.get_value(s, k, -> 3118 tz=getattr(series.dtype, 'tz', None)) 3119 except KeyError as e1: 3120 if len(self) > 0 and self.inferred_type in ['integer', 'boolean']: pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_value() pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_value() pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item() pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item() KeyError: 0 ```

130 个相见恨晚的超实用网站，一次性分享出来

相见恨晚的超实用网站 持续更新中。。。

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返回json示例 { "errcode":0,//0标识接口正常 "data":{ "date":"2020-01-30 07:47:23",//实时更新时间 "diagnosed":7736,//确诊人数 "suspect":12167,//疑是病例人数 "death":170,//死亡人数 "cur...

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