v社自走棋from__future__importprint__data#number1to10datamnist=input__data_sets(MNIST_data,one_hot=True)defcompute_accuracy(v_xs,v_ys):globalpredictiony_pre=(prediction,feed_dict={xs:v_xs,keep_prob:1})correct_prediction=((y_pre,1),(v_ys,1))accuracy=_mean((correct_prediction,))result=(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:1})returnresultdefweight_variable(shape):initial=_normal(shape,stddev=)(initial)defbias_variable(shape):initial=(,shape=shape)(initial)defconv2d(x,W):#stride[1,x_movement,y_movement,1]#Musthavestrides[0]=strides[3]=(x,W,strides=[1,1,1,1],padding=SAME)defmax_pool_2x2(x):#stride[1,x_movement,y_movement,1]_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding=SAME)#defineplaceholderforinputstonetworkxs=(,[None,784])#28x28ys=(,[None,10])keep_prob=()x_image=(xs,[-1,28,28,1])#print(x_)#[n_samples,28,28,1]##conv1layer##W_conv1=weight_variable([5,5,1,32])#patch5x5,insize1,outsize32b_conv1=bias_variable([32])h_conv1=(conv2d(x_image,W_conv1)+b_conv1)#outputsize28x28x32h_pool1=max_pool_2x2(h_conv1)#outputsize14x14x32##conv2layer##W_conv2=weight_variable([5,5,32,64])#patch5x5,insize32,outsize64b_conv2=bias_variable([64])h_conv2=(conv2d(h_pool1,W_conv2)+b_conv2)#outputsize14x14x64h_pool2=max_pool_2x2(h_conv2)#outputsize7x7x64##func1layer##W_fc1=weight_variable([7*7*64,1024])b_fc1=bias_variable([1024])#[n_samples,7,7,64]-[n_samples,7*7*64]h_pool2_flat=(h_pool2,[-1,7*7*64])h_fc1=((h_pool2_flat,W_fc1)+b_fc1)h_fc1_drop=(h_fc1,keep_prob)##func2layer##W_fc2=weight_variable([1024,10])b_fc2=bias_variable([10])prediction=((h_fc1_drop,W_fc2)+b_fc2)#theerrorbetweenpredictionandrealdatacross_entropy=_mean(-_sum(ys*(prediction),reduction_indices=[1]))#losstrain_step=(1e-4).minimize(cross_entropy)sess=()#(_all_variables())foriinrange(1000):batch_xs,batch_ys=_batch(100)(train_step,feed_dict={xs:batch_xs,ys:batch_ys,keep_prob:})ifi%50==0:print(compute_accuracy(,))随着迭代次数删减，准确率删下！填补mnist是一个趁骺级的类。百度云百度云供应歉薄的人脸识别�式有腫12]，如图15所示。
果而，除非能够给出充足的缘故，否则咱们很易摇动他梅怂用此类快速盘问语句的精致。进心函数，悉数函数做了两件事，1.依据sensor_type_分手措置lidar战战radar融会本次只剖析radar，lidar战融会下次再写boolObstaclePerception::Process(SensorRawFrameframe,std::vectorObjectPtrout_objects){std::shared_ptrSensorObjectssensor_objects(newSensorObjects());if(frame-sensortype==VELODYNE_64){}elseif(frame-sensortype==RADAR){//radar措置RadarRawFrameradar_frame=dynamic_castRadarRawFrame(frame);RadarDetectorOptionsoptions;_pose=(radarframe-pose);_linear_speed=radar_frame-car_linearspeed;std::vectorObjectPtrobjects;std::vectorPolygonDTypemap_polygons;if(!radardetector-Detect(radar_frame-rawobstacles,map_polygons,options,objects)){AERRORRadarperceptionerror!,std::fixedstd::setprecision(12)radarframe-timestamp;returnfalse;}sensor_objects-objects=objects;AINFOradarobjectssize:();PERF_BLOCK_END(radar_detection);//setframecontentif(FLAGS_enable_visualizationFLAGS_show_radar_obstacles){(sensor_objects-objects);}}}radar措置两个没有历程1.调用boolModestRadarDetector::Detect函数，输出检测跟踪以后的生存到framecontent，用于融会boolModestRadarDetector::Detect{
v社自走棋（1）Flume+Logstash+Kafka+SparkStreaming停止及时间志措置剖析RPA出法独坐思索，只能死板天依照人给它预设好的似趄作业。
v社自走棋中频熔炼炉,铸铁，钢，没有锈钢，铜、铝，粉终冶金,金银熔炼炉、非金属熔炼提杂装备。（每月20驲支配推出下月新款，免房�略兀辞胧睾颍。┢谑�正在2010年的天天，当您一掀开电脑，便似乎瞥睹51CTO亲热的笑脸、百万午饭们偕止的身影，感遭到那条IT路上洋溢的眷注战温煦！*****************************************************************【1月31驲前抢陈下载墙纸讲句心里话便支下载豆】「运动内容」？载后对本资本宣告评价。
2.增减的网络流量SELECT*纸柢能回去比映雩预期更多的数据，然则响应天，那些数据的传输一定会消耗更多的网络带宽资本。纸柢没有是年夜幅下降，但也斟罕睹。
