End-to-End Flow Monitoring with IPFIX

End-to-End Flow Monitoring with IPFIX

Byungjoon Lee1, Hyeongu Son2, Seunghyun Yoon1 and Youngseok Lee2

1 ETRI, NCP Team, Gajeong-Dong 161, Yuseong-Gu, Daejeon, Republic of Korea

{bjlee, shpyoon}@etri.re.kr

2 Chungnam National University, Gung-Dong, Yuseong-Gu, Daejeon, Republic of Korea

{hguson, lee}@etri.re.kr

Abstract. End-to-End (E2E) flow monitoring is useful for observing

performance of networks such as throughput, loss rate, and jitter. Typically,

E2E flow monitoring is carried out at end hosts with known tools such as iperf.

However, in a large-scale network, the end-host approach for performance

measurement may not be easily deployed because of expensive costs and high

administrative overheads. Therefore, in this paper, we propose a new E2E flow

monitoring method based on IP Flow Information eXport (IPFIX) that could

provide QoS metrics such as throughput, retransmission rate, delay, and jitter

for TCP/RTP flows. Especially, we present and evaluate a QoS monitoring

scheme for RTP flows using SIP. We have extended the IPFIX templates for

carrying QoS-related fields, and developed the E2E flow monitoring function

with the open source that could be embedded into routers. From experiments, it

was shown that the performance of TCP and RTP flows could be easily

examined with the IPFIX-based approach.

Keywords: End-to-end flow, IPFIX, Performance, Measurement, TCP, RTP

1 Introduction

An E2E flow in the Internet could be regarded as a sequence of packet arrivals consisting of single web session, a file transfer, a video stream, or a VoIP call. Recently, a lot of performance questions on an end-to-end flow are being raised by users as well as Internet Service Provider’s (ISP’s). Traditionally, a steady-state throughput of a long-lived TCP flow is considered to be a good indicator of the E2E flow performance, because most Internet traffic is associated with TCP protocol. Therefore, it is necessary to monitor the performance of TCP flows to correctly assess the end-to-end performance metrics of typical Internet applications such as Web downloading, email, and file exchanging.

On the other hand, although only the best-effort service is available within the current Internet, ISP’s are willing to provide the QoS-guaranteed service for the specialized applications such as MMoIP for maximizing the revenue. Most of the MMoIP services including VoIP and VoD have their own QoS requirements that are determined by the high-level Service Level Agreements (SLA) between the subscribers and the providers of the service. It is essential for ISP’s to monitor QoS parameters of MMoIP services for assessing whether the services are correctly

2 Byungjoon Lee, Hyeongu Son, Seunghyun Yoon and Youngseok Lee

delivered to the subscribers. Generally, it could be assumed that each QoS-critical Internet application is bound with a specific signaling mechanism like industry-proven and widely-accepted SIP or H.323 to collect charging-related information more easily.

Hence, in this paper, we focus on monitoring QoS metrics of TCP flows and RTP sessions established by a signaling protocol of SIP, because SIP is the most popular signaling protocol in current IP network. For TCP, we figure out TCP throughput and the number of retransmitted packets of each TCP flow. For RTP sessions, we measure throughput, delay, and jitter of the RTP media streams created by SIP signaling messages.

For the observation of the E2E flow performance metrics, we employ the IPFIX architecture [1], where routers with IPFIX-compliant traffic monitoring functions will monitor and export E2E flow information to the collector. In general, the IPFIX architecture is more useful than the end-host approach in a large-scale network, because routers of the E2E path could monitor the E2E flow and export the flow information to the collector. In the end-host approach, each end-host with specialized software like iperf should collect the QoS information and send it to the collector, which may not be scalable. In addition, in the router-based IPFIX approach, the problematic segments of the path that an E2E flow is traversing could be easily identified.

In the IPFIX architecture, the performance information of each TCP flow or multimedia stream will be captured and computed by routers. The collected information is assembled into flow records, and exported to an external collector by IPFIX protocol [2]. The key concept of IPFIX protocol is the flexible and extensible template architecture that can be useful for various traffic monitoring applications. The template can be easily extended to export QoS measurement results.

Hence, in this paper, we propose new IPFIX templates which provide throughput and the retransmission rate for TCP flows, and throughput, delay, and jitter for SIP/RTP multimedia streams. Then, we propose an RTP stream detection method using SIP signaling information. Through the experiments with the prototype system for monitoring E2E flow performance, it is seen that our approach could easily provide the performance metrics of E2E flows.

The remainder of this paper is organized as follows. The related work is introduced in section 2. In section 3, the architecture of our system is explained. The results of the experiments with the prototype are introduced in section 4. Section 5 concludes this paper.

Work

2 Related

In Internet2, there is a special working group called ‘Internet2 End to End Performance Initiative (E2Epi)’ [3] to establish the architecture and to propose E2E monitoring applications. Similarly, there has been an ‘Active Measurement Project (AMP)’ [4] that also addresses the issue of monitoring end-to-end performance using active measurement methods. However, with regard to the problem of detecting

End-to-End Flow Monitoring with IPFIX 3 problematic segments in the network, the measurement granularity for these projects is too coarse to give detailed flow-based monitoring results.

An architecture to monitor end-to-end performance of selected flows has been proposed in [5]. Subscribers of an ISP service can check whether their flows are being serviced correctly by querying a database which stores per-flow information exported by each meter (i.e., routers) using Netflow v5 protocol. However, the suggested scheme cannot deliver the detailed measurement results of the QoS parameters because the Netflow v5 protocol can export only a limited set of performance parameters.

Cisco nBar [6] suggests a mechanism to detect RTP flows using payload classification. This system can mark the detected flows to be shaped or policed for satisfying certain QoS requirements. However, this system does not provide a solution for end-to-end performance monitoring.

A method to monitor end-to-end QoS metrics of media streams associated with SIP signaling has been presented in [7]. This method combines active and passive measurement methods. For active measurement, monitoring probes are injected to the network by agent-side packet generators. The measurement results are collected with SNMP and saved as flow files at the SIP proxy. Therefore, user agents and SIP proxy server software should be modified to use this scheme. Thus, this may be rather unrealistic because it is difficult in the real network to modify the software of the participating network entities for the specific measurement methods.

A QoS measurement method [8] for RTP flows has been proposed by using nProbe

[9]. A new IPFIX template was introduced to export QoS monitoring results to an external collector. However, this work does not take SIP signaling into consideration. In this paper, we extend the work in [8] to extract RTP flow information from SIP SDP payloads.

3 The Proposed E2E Flow Monitoring Method

3.1 The IPFIX-based Architecture

The system architecture of the proposed method is described in Fig. 1.

Fig. 1. The proposed IPFIX-based system architecture

4 Byungjoon Lee, Hyeongu Son, Seunghyun Yoon and Youngseok Lee

As specified in [1], the IPFIX architecture consists of IPFIX devices and collector processes. IPFIX devices observe flows, meter flows, and export metered results for each flow to collector processes via the IPFIX protocol. Thus, we employ the IPFIX framework that consists of flow probes and a collector. As shown in Fig. 1, the flow probe can be deployed as either a function of a router or a specific system that captures packets and processes flows. A flow probe observes end-to-end flows using 5-tuples of packet headers, meters each flow, and exports the metered results to collector via the IPFIX protocol. The collector saves the exported information into DBMS. The saved information can be used later for further analysis or visualization.

As previously mentioned, the flow probe only observes TCP and RTP flows associated with SIP messages. For RTP flow observation, the flow probe extracts 5-tuples of RTP flows associated with each SIP packet by inspection SDP payloads of SIP INVITE/OK messages. The extracted information is used for RTP flow identification. For the TCP/RTP flows, the QoS metrics are also metered and exported.

3.2 The E2E Flow Monitoring Scenario

3.2.1 TCP Flows

In metering E2E performance parameters, it is assumed that every packet of a flow is captured without losses. Therefore, our flow monitoring scenario cannot be applied for the sampling-based measurement environment.

Given that every packet could be observed, it is straightforward to meter TCP performance parameters, that is, the number of retransmissions of a flow. We maintain a range of total bytes transferred for each flow. This range [0, N] (N is integer, and N > 0) will be tracked from TCP sequence numbers. Whenever a packet for the given flow is captured, we calculate the range of the payload bytes [m, m+k] (m and k are integers, and m, k > 0) for the packet from its sequence number and payload size. If the range overlaps (m < N) with the current total range [0, N], we conclude a retransmission has occurred. Therefore, for our method of monitoring TCP flows, following information is necessary for each flow.

The initial sequence number (ISN) is captured from the TCP SYN message. WRAP_CNT is for counting how many times the sequence number of a flow wrapped around the maximum sequence number (232-1) to zero. MAX_APN is the maximum value of Absolute Position Number (APN). By shifting the ISN of a flow to zero, we know the absolute location of each byte on the total N bytes transferred by the given flow. Therefore, when a new packet arrives, we calculate the APN of the first payload byte of the packet. If the value is smaller than MAX_APN of the flow, we assume that the retransmission has occurred. Though the similar phenomenon could be observed when a packet reordering event occurs, it is presumed that the packet reordering does not frequently happen, which are typically related with the routing table changes and non-FIFO queuing disciplines. Thus, the number of the retransmitted packets shows the quality of a flow.

End-to-End Flow Monitoring with IPFIX 5 3.2.2RTP Flows

Contrary to TCP flows, we need rather a sophisticated observation algorithm for RTP flows, because port numbers and IP addresses of RTP flows are not known in advance. As shown in Fig. 2, the 5-tuples of SIP-signaled RTP flows can be determined by inspecting the SDP payloads of SIP INVITE/OK packets exchanged between hosts [9]. IP address of the party who sent the SDP can be found at the line starts with ‘o=’, and the port numbers can be found at the lines starts with ‘m=’. As a call possibly includes several media streams, multiple port numbers can be found in a single SDP payload. The found 5-tuples are saved in a table, which is checked whenever a new RTP flow is detected. The QoS metrics are only metered for the RTP flows which have matching entry in the table.

Fig. 2. The 5-tuples of the RTP streams are calculated from the SDP payloads The packet loss ratio of a RTP flow is metered as shown in Algo. 1. In the algorithm, current_seq and prev_seq are the RTP sequence numbers of the current and previous packets. initial_seq is the RTP sequence number of the first packet of the flow. As the number of the lost RTP packets is the difference between current_seq and prev_seq, the value of pkt_loss_ratio is easily computed.

The jitter value of a RTP flow is metered by following a procedure specified in Algo. 2 in [12], where the current_time and prev_time are the timestamp values of the current and previous RTP packets. By tracking the value of variable ‘transit’, the delay is monitored.

(1)num_of_lost_pkt = current_seq – prev_seq

(2)prev_seq = current_seq

(3)num_of_tot_pkt = prev_seq + 1 – initial_seq

(4)pkt_loss_ratio = ( num_of_lost_pkt / num_of_tot_pkt ) * 100

Algo. 1. Calculating packet loss ratio of a RTP flow

(1)transit = current_time – prev_time

(2)j = | transit – last_transit |

(3)jitter += (j – jitter) / 16

(4)last_transit = transit

Algo. 2. Calculating jitter of a RTP flow

6 Byungjoon Lee, Hyeongu Son, Seunghyun Yoon and Youngseok Lee

3.3 IPFIX Templates for exporting E2E flows

As the exporting information set differs by the protocol, we use different IPFIX flow templates for each of the protocol: SIP, RTP and TCP. The fields defined in TCP and RTP templates constitute a set of QoS parameters. The values of the fields are calculated by the flow probe as presented in the previous section. The template used for SIP flows is shown in Fig. 3.

0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1

FlowSet ID = 0 Length = 36

Template ID = 260 Field Count = 7

0 SIP_CALL_ID = 130 50

0 SIP_RTP_SRC_PORT = 139 2

0 SIP_RTP_DST_PORT = 140 2

0 SIP_RTP_VIDEO_SRC_PORT = 141 2

0 SIP_RTP_VIDEO_DST_PORT = 142 2

0 SIP_RTP_SRC_IPV4_ADDR = 143 15

0 SIP_RTP_DST_IPV4_ADDR = 144 15

Fig. 3. The proposed IPFIX template for SIP messages

In Fig. 3, SIP_CALL_ID is an ID assigned by the flow probe to each SIP call. SIP_RTP_SRC_PORT and SIP_RTP_DST_PORT are source and destination port numbers assigned to the voice RTP flow. SIP_RTP_VIDEO_SRC_PORT and SIP_RTP_VIDEO_DST_PORT are source and destination port numbers assigned to the video RTP flow. SIP_RTP_SRC_IPV4_ADDR and SIP_RTP_DST_IPV4_ADDR are source and destination IP addresses of the SIP session. The packets exported by this template are used only for correlating each call with its constituent RTP flows.

0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1

FlowSet ID = 0 Length = 60

Template ID = 261 Field Count = 13

0 LAST_SWITCHED = 21 4

0 FIRST_SWITCHED =22 4

0 IN_BYTES = 1 4

0 IN_PKTS = 2 4

0 L4_SRC_PORT = 7 2

0 IPV4_SRC_ADDR = 27 4

0 L4_DST_PORT = 11 2

0 IPV4_DST_ADDR = 28 4

0 RTP_IN_JITTER = 154 4

0 RTP_IN_PKT_LOST = 156 4

0 RTP_IN_MAX_DELTA = 159 4

0 RTP_IN_MIN_DELTA = 161 4

0 RTP_IN_AVE_DELTA = 163 4

Fig. 4. The proposed IPFIX template for RTP flows

End-to-End Flow Monitoring with IPFIX 7 Fig. 4 is a IPFIX template for RTP flows. In this figure, LAST_SWITCHED and FIRST_SWITCHED are timestamp values demarking the start and end of a flow. IN_BYTE and IN_PKTS are counter values for the total volume and the number of packets observed. The values are used to calculate throughput of a flow. RTP_IN_JITTER is the jitter value of a flow. RTP_IN_PKT_LOST represents packet loss ratio of a flow. RTP_IN_MAX_DELTA, RTP_IN_MIN_DELTA, and RTP_IN_AVE_DELTA represent the maximum, minimum, and average delay.

0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1

FlowSet ID = 0 Length = 48

Template ID = 262 Field Count = 10

0 TCP_RETRAN_COUNT = 251 4

0 LAST_SWITCHED = 21 4

0 FIRST_SWITCHED =22 4

0 IN_BYTES = 1 4

0 IN_PKTS = 2 4

0 L4_SRC_PORT = 7 2

0 IPV4_SRC_ADDR = 27 4

0 L4_DST_PORT = 11 2

0 IPV4_DST_ADDR = 28 4

Fig. 5. TCP Template

Fig. 5 shows an IPFIX template for TCP flows. In this figure, TCP_RETRAN_COUNT is a counter for the number of retransmission. The fields are used to assess the transmission quality of a specific TCP flow.

4 Experiments

4.1 Environments

Fig. 6. Experimental environment

8 Byungjoon Lee, Hyeongu Son, Seunghyun Yoon and Youngseok Lee

The experimental environment is shown in Fig. 6. We set up two end nodes which are used for VoIP SIP soft-phone. TCP flows are also tested between these two machines. One end node is connected to the Internet through a commercial Wi-Fi access service. The other node is connected to the CNU campus network. We have monitored the end-to-end flows at two observation points: one is the access router and the other one is the border router of the CNU campus. The mirrored traffic is captured by flow exporters. The flow exporters send IPFIX messages to the external flow collector.

We used WengoPhone [13] for the VoIP experiment. The flow probe is a plain Linux box equipped with our modified version of nProbe. The end hosts and flow collector are PCs running Windows XP. As previously mentioned, MySQL DBMS version 4.0.26 [10] is installed at the flow collector machine.

4.2 The Prototype

The prototype of the flow probe is built on top of nProbe [9]. By extending the nProbe to use IPFIX templates previously mentioned, we added following new features to the nProbe 4.0.

-Supporting multiple templates: Previously, only a single IPFIX template can be used for metering. However, SIP-based RTP monitoring requires both the SIP template and RTP template to be used at once. Therefore, we modified nProbe to support the multi-template exporting function.

-Adding new fields to the SIP template: As the original SIP template implemented in nProbe 4.0 does not support fields related to video RTP flows, we added these video RTP-related fields to the SIP template.

To collect the IPFIX packets exported by the extended nProbe, we have implemented a simple IPFIX collector that simply converts the information contained in an IPFIX packet and saves the converted information into the MySQL [10] DBMS. The saved information is used for visualizing QoS of flows.

4.3 Results

First, Fig. 7 and 8 show the throughput and the number of retransmitted packets for a TCP flow observed at two flow probes. The congestion control behavior of the TCP sender is similarly seen at two flow probes, because there are no queueing delays at two routers in the campus network. The number of retransmitted packets belonging to a flow is generally similar but not exactly same at two flow probes, because the retransmitted packets are not included by the same flow. On average, it is shown that the throughput and the end-to-end QoS information of a TCP flow could be derived by our scheme.

End-to-End Flow Monitoring with IPFIX 9

Fig. 7. Throughput (bit rate) of a TCP flow observed by two flow probes installed at routers

Fig. 8. Retransmitted packets of a TCP flow observed by two flow probes installed at routers Secondly, the representative QoS metrics 1 of bit rate, delay, and jitter for a SIP/RTP flow are shown in Fig. 9, 10, and 11. The RTP stream generated by the PCM codec shows the constant bit rate of 81 Kbps. In the experiment, the observed QoS metrics of bit rate, delay, and jitter imply the good quality of voice traffic. Specifically, delays and jitters shown in Fig. 10 and 11 are slightly different at each

Fig. 9. Bit rates of a SIP/RTP flow observed by two flow probes installed at routers

1 The loss rate of the RTP flow is not shown because of very low packet loss rate and the page limit.

10 Byungjoon Lee, Hyeongu Son, Seunghyun Yoon and Youngseok Lee

Fig. 10. Average delays of a SIP/RTP flow observed by two flow probes installed at routers

Fig. 11. Jitter values of a SIP/RTP flow observed by two flow probes installed at routers

5. Conclusion & Future Work

In this paper, we presented an end-to-end performance measurement scheme using IPFIX and its applications on TCP and SIP/RTP flows. Our method is useful for detecting problematic network segments because multiple flow probes on the path are monitoring QoS information of E2E flows. Since our IPFIX-based scheme is based on passive measurement by routers, it does not require any special software or hardware to be set up at the end host. Hence, the proposed method could be easily deployed. However, our IPFIX-based QoS monitoring method may have an issue regarding high performance, because it assumes no packet loss for monitoring flows. It is widely known that loss-free packet capturing is difficult to achieve at the high-speed line rates. Since our proposal does not use RTCP sender reports which include the QoS metrics measured at the RTP end hosts, it could be extended to combine the flow information from both routers and end-hosts for detailed QoS information.

End-to-End Flow Monitoring with IPFIX 11 REFERENCE

[1]G. Sadasivan, N. Brownlee, B. Claise and J.Quittek, Architecture for IP Flow Information

Export, IETF Internet-Draft, draft-ietf-ipfix-architecture-12.

[2] B. Claise, Specification of the IPFIX protocol for the Exchange of IP Traffic Flow

Information. IETF Internet-Draft, draft-ietf-ipfix-protocol-24.txt

[3]E2E Performance Initiative, https://www.wendangku.net/doc/aa5394034.html,/

[4]NLANR AMP, https://www.wendangku.net/doc/aa5394034.html,/

[5]W. Song and D. Choi, Experiences in End-to-End Performance Monitoring on KOREN,

APNOMS 2006.

[6]Cisco nBar, https://www.wendangku.net/doc/aa5394034.html,/warp/public/cc/pd/iosw/prodlit/nbarw_wp.pdf

[7]T. Lindh and E. Roos, Monitoring of SIP-Based Communication using Signalling

Information for Performance Measurements, ICISP 2006.

[8]H. Son, B. Lee, S. Shin and Y. Lee, Flow-level QoS Monitoring of Multimedia Streaming

Traffic with IPFIX, KNOM 2007.

[9]nProbe, https://www.wendangku.net/doc/aa5394034.html,/nProbe.html

[10]MySQL, https://www.wendangku.net/doc/aa5394034.html,

[11]J. Rosenberg and H. Schulzrinne, An Offer/Answer Model with the Session Description

Protocol (SDP), IETF RFC 3264.

[12]H. Schulzrinne, S. Casner, R. Frederick and V. Jacobson, RTP: A Transport Protocol for

Real-Time Applications, IETF RFC 1889.

[13]WengoPhone, https://www.wendangku.net/doc/aa5394034.html,

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题库深度学习面试题型介绍及解析--第7期

1.简述激活函数的作用 使用激活函数的目的是为了向网络中加入非线性因素；加强网络的表示能力，解决线性模型无法解决的问题 2.那为什么要使用非线性激活函数？ 为什么加入非线性因素能够加强网络的表示能力？——神经网络的万能近似定理 ?神经网络的万能近似定理认为主要神经网络具有至少一个非线性隐藏层，那么只要给予网络足够数量的隐藏单元，它就可以以任意的精度来近似任何从一个有限维空间到另一个有限维空间的函数。 ?如果不使用非线性激活函数，那么每一层输出都是上层输入的线性组合；此时无论网络有多少层，其整体也将是线性的，这会导致失去万能近似的性质 ?但仅部分层是纯线性是可以接受的，这有助于减少网络中的参数。3.如何解决训练样本少的问题？ 1.利用预训练模型进行迁移微调（fine-tuning），预训练模型通常在特征上拥有很好的语义表达。此时，只需将模型在小数据集上进行微调就能取得不错的效果。CV 有 ImageNet，NLP 有 BERT 等。 2.数据集进行下采样操作，使得符合数据同分布。

3.数据集增强、正则或者半监督学习等方式来解决小样本数据集的训练问题。 4.如何提升模型的稳定性？ 1.正则化（L2, L1, dropout）：模型方差大，很可能来自于过拟合。正则化能有效的降低模型的复杂度，增加对更多分布的适应性。 2.前停止训练：提前停止是指模型在验证集上取得不错的性能时停止训练。这种方式本质和正则化是一个道理，能减少方差的同时增加的偏差。目的为了平衡训练集和未知数据之间在模型的表现差异。 3.扩充训练集：正则化通过控制模型复杂度，来增加更多样本的适应性。 4.特征选择：过高的特征维度会使模型过拟合，减少特征维度和正则一样可能会处理好方差问题，但是同时会增大偏差。 5.你有哪些改善模型的思路？ 1.数据角度 增强数据集。无论是有监督还是无监督学习，数据永远是最重要的驱动力。更多的类型数据对良好的模型能带来更好的稳定性和对未知数据的可预见性。对模型来说，“看到过的总比没看到的更具有判别的信心”。 2.模型角度

VFP常用函数大全整理

VFP常用函数大全整理 一.字符及字符串处理函数:字符及字符串处理函数的处理对象均为字符型数据,但其返回值类型各异. 1.取子串函数: 格式:substr(c,n1,n2) 功能:取字符串C第n1个字符起的n2个字符.返回值类型是字符型. 例:取姓名字符串中的姓. store \"王小风\" to xm ?substr(xm,1,2) 结果为:王 2.删除空格函数:以下3个函数可以删除字符串中的多余空格,3个函数的返回值均为字符型. trim(字符串):删除字符串的尾部空格 alltrim(字符串):删除字符串的前后空格 ltrim(字符串):删除字符串的前面的空格 例:去掉第一个字符串的尾空格后与第二个字符串连接 store \"abcd \" to x store \"efg\" to y ?trim(x)+y abcdefg 3.空格函数: 格式:space(n) 说明:该函数的功能是产生指定个数的空格字符串(n用于指定空格个数). 例:定义一个变量dh,其初值为8个空格 store space(8) to dh 4.取左子串函数: 格式:left(c,n) 功能:取字符串C左边n个字符. 5.取右子串函数: 格式:right(c,n) 功能:取字符串c右边的n个字符 例:a=\"我是中国人\" ?right(a,4) 国人 ?left(a,2) 我 6.empty(c):用于测试字符串C是否为空格. 7.求子串位置函数: 格式:At(字符串1,字符串2) 功能:返回字符串1在字符串2的位置 例:?At(\"教授\",\"副教授\") 2

8.大小写转换函数: 格式: lower(字符串) upper(字符串) 功能:lower()将字符串中的字母一律变小写;upper()将字符串中的字母一律变大写 例: bl=\"FoxBASE\" ?lower(bl)+space(2)+upper(bl) foxbase FOXBASE 9.求字符串长度函数: 格式:len(字符串) 功能:求指定字符串的长度 例:a=\"中国人\" ?len(a) 6 二.数学运算函数: 1.取整函数: 格式:int(数值) 功能:取指定数值的整数部分. 例:取整并显示结果 ?int(25.69) 25 2.四舍五入函数: 格式:round(数值表达式,小数位数) 功能:根据给出的四舍五入小数位数,对数值表达式的计算结果做四舍五入处理 例:对下面给出的数四舍五入并显示其结果 ?round(3.14159,4),round(2048.9962,0),round(2048.9962,-3) 3.1416 2049 2000 3.求平方根函数: 格式:sqrt(数值) 功能:求指定数值的算术平方根 例:?sqrt(100) 10 4.最大值、最小值函数: 格式： Max(数值表达式1，数值表达式2） Min(数值表达式1，数值表达式2） 功能：返回两个数值表达式中的最大值和最小值 例：

人工智能实践：Tensorflow笔记 北京大学 7 第七讲卷积网络基础 (7.3.1) 助教的Tenso

Tensorflow笔记：第七讲 卷积神经网络 本节目标：学会使用CNN实现对手写数字的识别。 7.1 √全连接NN：每个神经元与前后相邻层的每一个神经元都有连接关系，输入是特征，输出为预测的结果。 参数个数：∑（前层×后层+后层） 一张分辨率仅仅是28x28的黑白图像，就有近40万个待优化的参数。现实生活中高分辨率的彩色图像，像素点更多，且为红绿蓝三通道信息。 待优化的参数过多，容易导致模型过拟合。为避免这种现象，实际应用中一般不会将原始图片直接喂入全连接网络。 √在实际应用中，会先对原始图像进行特征提取，把提取到的特征喂给全连接网络，再让全连接网络计算出分类评估值。

例：先将此图进行多次特征提取，再把提取后的计算机可读特征喂给全连接网络。 √卷积Convolutional 卷积是一种有效提取图片特征的方法。一般用一个正方形卷积核，遍历图片上的每一个像素点。图片与卷积核重合区域内相对应的每一个像素值乘卷积核内相对应点的权重，然后求和，再加上偏置后，最后得到输出图片中的一个像素值。 例：上面是5x5x1的灰度图片，1表示单通道，5x5表示分辨率，共有5行5列个灰度值。若用一个3x3x1的卷积核对此5x5x1的灰度图片进行卷积，偏置项

b=1，则求卷积的计算是：(-1)x1+0x0+1x2+(-1)x5+0x4+1x2+(-1)x3+0x4+1x5+1=1（注意不要忘记加偏置1）。 输出图片边长=（输入图片边长–卷积核长+1）/步长，此图为：（5 – 3 + 1）/ 1 = 3，输出图片是3x3的分辨率，用了1个卷积核，输出深度是1，最后输出的是3x3x1的图片。 √全零填充Padding 有时会在输入图片周围进行全零填充，这样可以保证输出图片的尺寸和输入图片一致。 例：在前面5x5x1的图片周围进行全零填充，可使输出图片仍保持5x5x1的维度。这个全零填充的过程叫做padding。 输出数据体的尺寸=(W?F+2P)/S+1 W：输入数据体尺寸，F：卷积层中神经元感知域，S：步长，P：零填充的数量。 例：输入是7×7，滤波器是3×3，步长为1，填充为0，那么就能得到一个5×5的输出。如果步长为2，输出就是3×3。 如果输入量是32x32x3，核是5x5x3，不用全零填充，输出是（32-5+1）/1=28，如果要让输出量保持在32x32x3，可以对该层加一个大小为2的零填充。可以根据需求计算出需要填充几层零。32=（32-5+2P）/1 +1，计算出P=2，即需填充2

常用函数 类参考

全局函数1、common.func.php 公用函数 获得当前的脚本网址 function GetCurUrl() 返回格林威治标准时间 function MyDate($format='Y-m-d H:i:s',$timest=0) 把全角数字转为半角 function GetAlabNum($fnum) 把含HTML的内容转为纯text function Html2Text($str,$r=0) 把文本转HTML function Text2Html($txt) 输出Ajax头 function AjaxHead() 中文截取2，单字节截取模式 function cn_substr($str,$slen,$startdd=0) 把标准时间转为Unix时间戳 function GetMkTime($dtime) 获得一个0000-00-00 00:00:00 标准格式的时间 function GetDateTimeMk($mktime) 获得一个0000-00-00 标准格式的日期 function GetDateMk($mktime) 获得用户IP function GetIP() 获取拼音以gbk编码为准 function GetPinyin($str,$ishead=0,$isclose=1)

dedecms通用消息提示框 function ShowMsg($msg,$gourl,$onlymsg=0,$limittime=0) 保存一个cookie function PutCookie($key,$value,$kptime=0,$pa="/") 删除一个cookie function DropCookie($key) 获取cookie function GetCookie($key) 获取验证码 function GetCkVdValue() 过滤前台用户输入的文本内容 // $rptype = 0 表示仅替换html标记 // $rptype = 1 表示替换html标记同时去除连续空白字符// $rptype = 2 表示替换html标记同时去除所有空白字符// $rptype = -1 表示仅替换html危险的标记 function HtmlReplace($str,$rptype=0) 获得某文档的所有tag function GetTags($aid) 过滤用于搜索的字符串 function FilterSearch($keyword) 处理禁用HTML但允许换行的内容 function TrimMsg($msg) 获取单篇文档信息 function GetOneArchive($aid)

人工智能实践：Tensorflow笔记 北京大学 4 第四讲神经网络优化 (4.6.1) 助教的Tenso

Tensorflow笔记：第四讲 神经网络优化 4.1 √神经元模型：用数学公式表示为：f(∑i x i w i+b)，f为激活函数。神经网络是以神经元为基本单元构成的。 √激活函数：引入非线性激活因素，提高模型的表达力。 常用的激活函数有relu、sigmoid、tanh等。 ①激活函数relu: 在Tensorflow中，用tf.nn.relu()表示 r elu()数学表达式 relu()数学图形 ②激活函数sigmoid：在Tensorflow中，用tf.nn.sigmoid()表示 sigmoid ()数学表达式 sigmoid()数学图形 ③激活函数tanh：在Tensorflow中，用tf.nn.tanh()表示 tanh()数学表达式 tanh()数学图形 √神经网络的复杂度：可用神经网络的层数和神经网络中待优化参数个数表示 √神经网路的层数：一般不计入输入层，层数 = n个隐藏层 + 1个输出层

√神经网路待优化的参数：神经网络中所有参数w 的个数 + 所有参数b 的个数 例如： 输入层 隐藏层 输出层 在该神经网络中，包含1个输入层、1个隐藏层和1个输出层，该神经网络的层数为2层。 在该神经网络中，参数的个数是所有参数w 的个数加上所有参数b 的总数，第一层参数用三行四列的二阶张量表示（即12个线上的权重w ）再加上4个偏置b ；第二层参数是四行两列的二阶张量（）即8个线上的权重w ）再加上2个偏置b 。总参数 = 3*4+4 + 4*2+2 = 26。 √损失函数（loss ）：用来表示预测值（y ）与已知答案（y_）的差距。在训练神经网络时，通过不断改变神经网络中所有参数，使损失函数不断减小，从而训练出更高准确率的神经网络模型。 √常用的损失函数有均方误差、自定义和交叉熵等。 √均方误差mse ：n 个样本的预测值y 与已知答案y_之差的平方和，再求平均值。 MSE(y_, y) = ?i=1n (y?y_) 2n 在Tensorflow 中用loss_mse = tf.reduce_mean(tf.square(y_ - y)) 例如： 预测酸奶日销量y ，x1和x2是影响日销量的两个因素。 应提前采集的数据有：一段时间内，每日的x1因素、x2因素和销量y_。采集的数据尽量多。 在本例中用销量预测产量，最优的产量应该等于销量。由于目前没有数据集，所以拟造了一套数据集。利用Tensorflow 中函数随机生成 x1、 x2，制造标准答案y_ = x1 + x2，为了更真实，求和后还加了正负0.05的随机噪声。 我们把这套自制的数据集喂入神经网络，构建一个一层的神经网络，拟合预测酸奶日销量的函数。

数据库常用函数

数据库常用函数

一、基础 1、说明：创建数据库 CREATE DATABASE database-name 2、说明：删除数据库 drop database dbname 3、说明：备份和还原 备份：exp dsscount/sa@dsscount owner=dsscount file=C:\dsscount_data_backup\dsscount.dmp log=C:\dsscount_data_backup\outputa.log 还原：imp dsscount/sa@dsscount file=C:\dsscount_data_backup\dsscount.dmp full=y ignore=y log=C:\dsscount_data_backup\dsscount.log statistics=none 4、说明：创建新表 create table tabname(col1 type1 [not null] [primary key],col2 type2 [not null],..) CREATE TABLE ceshi(id INT not null identity(1,1) PRIMARY KEY,NAME VARCHAR(50),age INT) id为主键，不为空，自增长 根据已有的表创建新表： A：create table tab_new like tab_old (使用旧表创建新表) B：create table tab_new as select col1,col2… from tab_old definition only 5、说明：删除新表 drop table tabname 6、说明：增加一个列 Alter table tabname add column col type 注：列增加后将不能删除。DB2中列加上后数据类型也不能改变，唯一能改变的是增加varchar类型的长度。 7、说明：添加主键： Alter table tabname add primary key(col) 说明：删除主键： Alter table tabname drop primary key(col) 8、说明：创建索引：create [unique] index idxname on tabname(col….) 删除索引：drop index idxname 注：索引是不可更改的，想更改必须删除重新建。 9、说明：创建视图：create view viewname as select statement 删除视图：drop view viewname 10、说明：几个简单的基本的sql语句 选择：select * from table1 where 范围 插入：insert into table1(field1,field2) values(value1,value2) 删除：delete from table1 where 范围 更新：update table1 set field1=value1 where 范围

人工智能tensorflow实验报告

一、软件下载 为了更好的达到预期的效果，本次tensorflow开源框架实验在Linux环境下进行，所需的软件及相关下载信息如下： 1.CentOS 软件介绍： CentOS 是一个基于Red Hat Linux 提供的可自由使用源代码的企业级Linux 发行版本。每个版本的CentOS都会获得十年的支持（通过安全更新方式）。新版本的CentOS 大约每两年发行一次，而每个版本的CentOS 会定期（大概每六个月）更新一次，以便支持新的硬件。这样，建立一个安全、低维护、稳定、高预测性、高重复性的Linux 环境。CentOS是Community Enterprise Operating System的缩写。CentOS 是RHEL（Red Hat Enterprise Linux）源代码再编译的产物，而且在RHEL的基础上修正了不少已知的Bug ，相对于其他Linux 发行版，其稳定性值得信赖。 软件下载： 本次实验所用的CentOS版本为CentOS7,可在CentOS官网上直接下载DVD ISO镜像文件。 下载链接： https://www.wendangku.net/doc/aa5394034.html,/centos/7/isos/x86_64/CentOS-7-x86_64-DVD-1611.i so. 2.Tensorflow 软件介绍： TensorFlow是谷歌基于DistBelief进行研发的第二代人工智能学习系统，其命名来源于本身的运行原理。Tensor（张量）意味着N维数组，Flow（流）意味着基于数据流图的计算，TensorFlow为张量从流图的一端流动到另一端计算过程。TensorFlow是将复杂的数据结构传输至人工智能神经网中进行分析和处理过程的系统。TensorFlow可被用于语音识别或图像识别等多项机器深度学习领域，对2011年开发的深度学习基础架构DistBelief进行了各方面的改进，它可在小到一部智能手机、大到数千台数据中心服务器的各种设备上运行。TensorFlow将完全开源，任何人都可以用。

EXCEL常用分类函数

EXCEL常用分类函数 （注意有些函数只有加载“分析工具库”以后方能使用） 一、查找和引用函数 ROW 用途：返回给定引用的行号。 语法：ROW(reference)。 Reference为需要得到其行号的单元格或单元格区域。 实例：公式“=ROW(A6)”返回6，如果在C5单元格中输入公式“=ROW()”，其计算结果为5。 COLUMN 用途：返回给定引用的列标。 语法：COLUMN(reference)。 参数：Reference为需要得到其列标的单元格或单元格区域。如果省略reference，则假定函数COLUMN是对所在单元格的引用。如果reference为一个单元格区域，并且函数COLUMN作为水平数组输入，则COLUMN函数将reference中的列标以水平数组的形式返回。 实例：公式“=COLUMN(A3)”返回1，=COLUMN(B3:C5)返回2。 CELL 用途：返回某一引用区域的左上角单元格的格式、位置或内容等信息 IS类函数 用途：用来检验数值或引用类型的函数。它们可以检验数值的类型并根据参数的值返回TRUE或FALSE。ISBLANK(value)、ISNUMBER(value)、ISTEXT(value)和ISEVEN(number)、ISODD(number)。 ADDRESS 用途：以文字形式返回对工作簿中某一单元格的引用。 语法：ADDRESS(row_num，column_num，abs_num，a1，sheet_text) 参数：Row_num是单元格引用中使用的行号;Column_num是单元格引用中使用的列标;Abs_num指明返回的引用类型(1或省略为绝对引用，2绝对行号、相对列标，3相对行号、绝对列标，4是相对引用);A1是一个逻辑值，它用来指明是以A1或R1C1返回引用样式。如果A1为TRUE或省略，函数ADDRESS返回A1样式的引用;如果A1为FALSE，函数ADDRESS返回R1C1样式的引用。Sheet_text为一文本，指明作为外部引用的工作表的名称，如果省略sheet_text，则不使用任何工作表的名称。 实例：公式“=ADDRESS(1，4，4，1)”返回D1。 CHOOSE 用途：可以根据给定的索引值，从多达29个待选参数中选出相应的值或操作。 语法：CHOOSE(index_num，value1，value2，...)。

TensorFlow编程指南 嵌入

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vf常用函数

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韦伯数按下式求得： We = 30cm * (187.8 cm/s)^2 * 1gm/cc / (73gm/s^2) = 1.45 x 104 再者，大的邦德数和大的韦伯数表明，与重力和惯性力相比，表面张力可忽略。模型是这种情况时，不考虑表面张力。 问题的大小（模型运行的时间）可以利用堰中心顺水流平面的对称特性进行简化。因此，我们仅仅需要模拟整个范围的一部分（即堰的后半部分），就可也得到堰的全部信息。我们已经对问题进行了简化，下面是如何建立这些条件，如何确定几何条件，利用flow3d求解问题。 建模 总体参数 点击“Model Setup”表的“General”表，“General”是确定整个问题的参数，如结束时间、结束条件、界面追踪，流体模式，液体的数量，提示选项，单位及精度。 对本教程，我们是想看流场，当液体达到几乎稳定状态时，它的时间是 1.0s。因此，通常设定结束时间为1.0s。对一个实际问题，可能运行这种模拟的时间会更长一些。但是，我们感兴趣的是速度，对于本运行，我们限定时间。在“Simulation units”标题菜单中，选CGS单位（厘米。克。秒），其它设置采用缺省设置。 在“总信息表Global tab”的底部注释中，你可以在第一行为问题指定一个名字。名字会出现在所有输出文件和图形上。本例名称为“Flow over a Weir”（过堰流体）。 建立几何体Geometry Setup 我们将添加元件定义堰体。首先，我们输入一个已有的STL文件，weri1.stl，该文件放在目录“c:\flow3d\gui\stl_lib”。切换到“几何与分网Meshing & Geometry ”表，单击工具条STL图标，会打开标题为“几何Geometry”的对话框，点击添加，打开对话框，找到并选择weri1.stl。在“Geometry File”点击ok，接受缺省设置。在之后出现的添加部件对话框中接受缺省设置。现在STL文件已经输入，并且出现在工作空间中。输入文件也被列在树形结构表中。 下面，我们将通过“FLOW-3D”简单建模创建另一个组件，来添加上游水库河床。在工具栏点击box（盒子）图标，盒子对话框显示如图2。

损失函数 Losses - Keras 中文文档

Docs ? 损失函数 Losses 损失函数的使用 损失函数（或称目标函数、优化评分函数）是编译模型时所需的两个参数之一： https://www.wendangku.net/doc/aa5394034.html,pile(loss='mean_squared_error', optimizer='sgd') from keras import losses https://www.wendangku.net/doc/aa5394034.html,pile(loss=losses.mean_squared_error, optimizer='sgd') 你可以传递一个现有的损失函数名，或者一个 T ensorFlow/Theano 符号函数。该符号函数为每个数据点返回一个标量，有以下两个参数: y_true: 真实标签。T ensorFlow/Theano 张量。 y_pred: 预测值。T ensorFlow/Theano 张量，其 shape 与 y_true 相同。 实际的优化目标是所有数据点的输出数组的平均值。 有关这些函数的几个例子，请查看losses source。 可用损失函数 mean_squared_error mean_squared_error(y_true, y_pred) mean_absolute_error mean_absolute_error(y_true, y_pred) mean_absolute_percentage_error mean_absolute_percentage_error(y_true, y_pred) mean_squared_logarithmic_error

mean_squared_logarithmic_error(y_true, y_pred) squared_hinge squared_hinge(y_true, y_pred) hinge hinge(y_true, y_pred) categorical_hinge categorical_hinge(y_true, y_pred) logcosh logcosh(y_true, y_pred) 预测误差的双曲余弦的对数。 对于小的x，log(cosh(x))近似等于(x ** 2) / 2。对于大的x，近似于abs(x) - log(2)。这表示 'logcosh' 与均方误差大致相同，但是不会受到偶尔疯狂的错误预测的强烈影响。 参数 y_true: 目标真实值的张量。 y_pred: 目标预测值的张量。 返回 每个样本都有一个标量损失的张量。 categorical_crossentropy categorical_crossentropy(y_true, y_pred)

Excel30个常用函数

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3、MATCH函数 主要功能：返回在指定方式下与指定数值匹配的数组中元素的相应位置。 使用格式：MATCH(需要在数据表中查找的数值, 所要查找的数值的连续单元格区域, 查找方式的值（-1、0或1）) 例：标的“甲”在B1:B7中的位置序号为2 ※INDEX和MATCH经常配合使用以应用了查找引用功能，作用与VLOOKUP类似，但能够更轻松的进行从右到左的转换。 4、IF函数 主要功能：根据对指定条件的逻辑判断的真假结果，返回相对应的内容。 使用格式：=IF(逻辑判断表达式, 成立返回值，不成立返回值) 此函数为嵌套型函数，Microsoft Excel 2010版最多可嵌套64层，通常会与其它函数配合使用。 例：根据大类，分别从表二，表三中匹配相应的值。