WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function. WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_1 WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_2 WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.decay WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.learning_rate

Iteration method for detecting disease genes in terms of the integration of the cellular compartment information with the protein-protein interaction data

本研究提出了一个名为IMIDG（Iterative Method for Identifying Disease Genes）的新框架，该框架旨在通过整合蛋白质相互作用（Protein-Protein Interaction, PPI）网络中的细胞定位信息来提高疾病基因识别的准确性...

Packt.TensorFlow.Deep.Learning.Projects.2018.rar

By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with ...

Anchor Box Strategy in YOLOv10: The Foundation for Optimizing Object Detection, Enhancing Model ...

# The Anchoring Strategy in YOLOv10: The Cornerstone of Optimizing Object Detection, Enhancing Model Accuracy # 1. An Overview of Object Detection Object detection is a fundamental task in computer ...

这一段讲的是什么：Abstract—A recent trojan attack on deep neural network (DNN) models is one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker’s chosen trojan trigger. Since the trojan trigger is a secret guarded and exploited by the attacker, detecting such trojan inputs is a challenge, especially at run-time when models are in active operation. This work builds STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system and focuses on vision system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of predicted classes for perturbed inputs from a given deployed model—malicious or benign. A low entropy in predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input—a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on three popular and contrasting datasets: MNIST, CIFAR10 and GTSRB. We achieve an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for different types of triggers. Using CIFAR10 and GTSRB, we have empirically achieved result of 0% for both FRR and FAR. We have also evaluated STRIP robustness against a number of trojan attack variants and adaptive attacks. Index Terms—Trojan attack, Backdoor attack

这段摘要讲述了关于深度神经网络（DNN）模型的特洛伊木马攻击。特洛伊攻击是一种数据污染攻击的变种，通过利用学习模型的难以解释性，在DNN模型中创建一个有效的后门，以便将任何使用攻击者选择的特洛伊触发器进行...

_thread.start_new_thread(self.sleepings, (event,)) AttributeError: 'Fatigue_detecting' object has no attribute 'sleepings'

这个问题看起来是一个 Python 语言中的错误提示。根据错误提示信息，可以得出结论：在代码中的某个地方，使用了一个名为 'sleepings' 的方法或属性，但是在当前对象中并没有这个方法或属性。因此，可能需要仔细...

# Edge Impulse - OpenMV Object Detection Example import sensor, image, time, os, tf, math, uos, gc sensor.reset() # Reset and initialize the sensor. sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE) sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) sensor.set_windowing((240, 240)) # Set 240x240 window. sensor.skip_frames(time=2000) # Let the camera adjust. net = None labels = None min_confidence = 0.5 try: # load the model, alloc the model file on the heap if we have at least 64K free after loading net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (641024))) except Exception as e: raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')') try: labels = [line.rstrip('\n') for line in open("labels.txt")] except Exception as e: raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')') colors = [ # Add more colors if you are detecting more than 7 types of classes at once. (255, 0, 0), ( 0, 255, 0), (255, 255, 0), ( 0, 0, 255), (255, 0, 255), ( 0, 255, 255), (255, 255, 255), ] clock = time.clock() while(True): clock.tick() img = sensor.snapshot() # detect() returns all objects found in the image (splitted out per class already) # we skip class index 0, as that is the background, and then draw circles of the center # of our objects for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence 255), 255)])): if (i == 0): continue # background class if (len(detection_list) == 0): continue # no detections for this class? print(" %s " % labels[i]) for d in detection_list: [x, y, w, h] = d.rect() center_x = math.floor(x + (w / 2)) center_y = math.floor(y + (h / 2)) print('x %d\ty %d' % (center_x, center_y)) img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2) print(clock.fps(), "fps", end="\n\n")

在这段代码中，首先通过 tf.load() 函数加载了名为 "trained.tflite" 的模型文件，并根据可用的内存动态选择将模型文件加载到堆还是加载到闪存。然后通过读取 "labels.txt" 文件加载了标签。接下来，代码进入一...

按照你说的做，为什么会报错如下：czj@czj-virtual-machine:~/build$ cmake .. -G Ninja CMake Warning (dev) in CMakeLists.txt: No project() command is present. The top-level CMakeLists.txt file must contain a literal, direct call to the project() command. Add a line of code such as project(ProjectName) near the top of the file, but after cmake_minimum_required(). CMake is pretending there is a "project(Project)" command on the first line. This warning is for project developers. Use -Wno-dev to suppress it. -- The C compiler identification is GNU 9.4.0 -- The CXX compiler identification is GNU 9.4.0 -- Check for working C compiler: /usr/bin/cc -- Check for working C compiler: /usr/bin/cc -- works -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Detecting C compile features -- Detecting C compile features - done -- Check for working CXX compiler: /usr/bin/c++ -- Check for working CXX compiler: /usr/bin/c++ -- works -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Detecting CXX compile features -- Detecting CXX compile features - done -- Configuring done -- Generating done -- Build files have been written to: /home/czj/build czj@czj-virtual-machine:~/build$ ninja ninja: no work to do. czj@czj-virtual-machine:~/build$ sudo ninja install [sudo] czj 的密码： ninja: error: unknown target 'install'

根据你提供的信息，可能是因为在使用 Ninja 构建系统时，没有配置 install 目标。你可以检查一下 CMakeLists.txt 文件中是否添加了 install 目标。如果没有，你可以添加以下代码： install(TARGETS ...

There is a light sensor on your picoprobe development board. This question asks you to use this sensor to measure the ambient light and to control the three LEDs on the picoprobe board to make a night light. Configure your program to perform the following steps, after printing the message from Question 1. The steps should be repeated every 1 second. raw ADC result in the format shown below: Light Sensor: Raw: _ (insert the actual value) (15 marks) (b) Convert the raw value to a voltage and print out the voltage that you calculated in the format displayed below. At least one decimal place is required. Voltage: _ V (insert the actual value) (10 marks) (c) Use this voltage to control the three-colour LED to show a white colour, when the light sensor is covered by a piece of paper (e.g. when the room is getting dark). The white-coloured light will go off, as soon as the ambient light is high again, i.e. when the light sensor is exposed to normal room light. This is reminiscent of an automatic night light.

Sure, here's how you can use the light sensor on the picoprobe development board... The threshold voltage for detecting darkness is set at 1.0 V in this example, but you can adjust it to suit your needs.

ERROR [08001] [IBM] SQL30081N A communication error has been detected. Communication protocol being used: "TCP/IP". Communication API being used: "SOCKETS". Location where the error was detected: "10.73.99.11". Communication function detecting the err

or: "connect". Protocol specific error code(s): "10061", "*", "*". SQLSTATE=08001 这是一个数据库连接错误，可能是由于网络故障或数据库服务器故障引起的。建议检查网络连接是否正常，数据库服务器是否运行，...

以下是一篇即将投稿Minerals期刊（MDPI出版社）的论文初稿的部分内容，请按照该期刊对论文格式的要求，将以下内容进行压缩凝练（注意：可对内容进行删减，对错误进行修正，对语句顺序进行调整，符合美式英语标准，符合英语母语者语言习惯，句子简明易懂，术语使用准确，保留文章结构、不偏离论文主要内容）： Rocks and ore components directly enter the soil and water system sediments through physical weathering and chemical weathering, and the geochemical anomalies originally present in the rocks further spread with the entry into the soil or directly into the water system, forming soil anomalies and water system sediment anoma-lies.Geochemical anomaly detection is essentially the detection of signal anomalies in geochemical data, which refers to finding out the anomalous distribution of chemical elements themselves and the anomalous distribution of multiple elements in combination through feature extraction and analysis processing of geochemical data in the study area, and reflecting the mineral distribution through the distribution of geochemical ele-ments.Through the method of geochemical anomaly finding, the detected anomalies may contain information indicating specific minerals, which facilitates the rapid tracing of prospective areas and favorable areas for mineralization, identifies possible mineralizing elements and distribution characteristics in the work area, provides basic information for the strategic deployment of mineralization search, and provides good indications for later mineralization search.

Rocks and ore components enter soil and water system sediments through physical and chemical weathering, causing the spread of geochemical anomalies. Detecting geochemical anomalies involves ...

class PolygonZone: """ A class for defining a polygon-shaped zone within a frame for detecting objects. Attributes: polygon (np.ndarray): A polygon represented by a numpy array of shape (N, 2), containing the x, y coordinates of the points. frame_resolution_wh (Tuple[int, int]): The frame resolution (width, height) triggering_position (Position): The position within the bounding box that triggers the zone (default: Position.BOTTOM_CENTER) current_count (int): The current count of detected objects within the zone mask (np.ndarray): The 2D bool mask for the polygon zone """ def init( self, polygon: np.ndarray, frame_resolution_wh: Tuple[int, int], triggering_position: Position = Position.BOTTOM_CENTER, ): self.polygon = polygon.astype(int) self.frame_resolution_wh = frame_resolution_wh self.triggering_position = triggering_position self.current_count = 0 width, height = frame_resolution_wh self.mask = polygon_to_mask( polygon=polygon, resolution_wh=(width + 1, height + 1) )

这是一个名为 PolygonZone 的类，用于定义帧中的多边形区域，用于检测对象。该类具有以下属性： - polygon：一个形状为 (N, 2) 的numpy数组，表示由 x 和 y 坐标组成的多边形的点。...

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