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(filters=256, kernel_size=5, padding=‘same‘, activation=‘relu‘, input_shape=(time_window_size, 1))) model.add(GlobalMaxPool1D()) model.add(Dense(units=time_window_size, activation=‘linear‘)) model.compile(optimizer=‘adam‘, loss=‘mean_squared_error‘, metrics=[metric]) print(model.summary()) return modelSet the output to your own. The exception points are the points with a larger predicted error deviation of the 90%. Keras-
http://blog.csdn.net/pipisorry/article/details/44783647Machine learning machines Learning-andrew NG Courses Study notesAnomaly Detection anomaly DetectionThe motive of problem motivation problemAnomaly Detection ExampleApplycation of ano
Deep learning target detection (object detection) series (i) r-cnnDeep learning target detection (object detection)
that the accuracy rate of YOLO in detecting small targets is about 8~10% than R-CNN, and the accuracy rate is higher than r-cnn in the detection of large targets. The accuracy of Fast-r-cnn+yolo is the highest, and the accuracy rate is 2.3% higher than that of FAST-R-CNN.5.4 SummaryYolo is a convolutional neural network that supports end-to-end training and testing, and can detect and recognize multiple targets in images under the premise of guarante
9 Anomaly Detection9.1 Density Estimation9.1.1 Problem MotivationAnomaly detection (Density estimation) is a common application of machine learning and is mainly used for unsupervised learning, but in some ways it is similar to supervised learning.The most common application of ano
each step in depth.Developing andevaluating an Anomaly Detection SystemWe will find it very important to evaluate a learning algorithm with a numerical standard, we can try to join a feature to evaluate it, and then remove the feature to evaluate it again, so that the effect of feature on the learning algorithm is obt
DDoS attacks are essentially time-series data, and the data characteristics of t+1 moments are strongly correlated with T-moments, so it is necessary to use HMM or CRF for detection! --and a sentence of the word segmentation algorithm CRF no difference!Note: Traditional DDoS detection is directly based on the IP data s
CNN operation, the calculation is still very large, many of which are in fact repeated calculation; SVM model: And it is a linear model, it is obviously not the best choice when labeling data is not missing; Training test is divided into multiple steps: Regional nomination, feature extraction, classification, regression are disconnected training process, intermediate data also need to be saved separately; The space and time cost of traini
per second (selective Search+fast r-cnn is 2~3s one). It is important to note that the latest version has combined the RPN network with the Fast R-CNN network-the proposal of RPN acquired directly to the ROI pooling layer, which is really the framework for using a CNN network for end-to-end target detection. Summary: Faster R-CNN has been separating the region proposal and CNN classifications together, using an end-to-end network for target
, 2, 3,1/2,1/3}, for aspect ratio = 1, add an additional default box, the size of the box. For each default box, the width, height, and center points are calculated as follows:4. Hard negative miningAfter matching, many default boxes are negative samples, which will result in a positive sample, negative sample imbalance, training difficult to converge. Therefore, the paper sorts the negative samples according to the confidence level, selects the highest ones, and guarantees that the proportion o
One of the target detection (traditional algorithm and deep learning source learning) This series of writing about target detection, including traditional algorithms and in-depth learning
, each location by 0 or 1 classification (to determine whether the interception of the image of the object to be detected). Select a larger window to repeat the above actions.Sliding window for convolution (improvements to the algorithm above)Convert the fully connected layer into a convolution layer:Principle: The video is that, from a mathematical point of view, the conversion of the convolution layer and the full join layer, each node in 400 nodes have a 5x5x16 dimension of the filter, these
Depth learning target detection (object detection) series (i) r-cnnDepth learning target detection (object detection) series (ii) spp-netDep
layer of the neural network can be used as a linear classifier, and then we can replace it with a classifier with better performance. During the study, we can find that adding the features obtained by automatic learning to the original features can greatly improve the accuracy, and even make the classification problem better than the current best classification algorithm! There are some variants of autoencoder. Here we will briefly introduce two: Spa
Written before: busy, always in a walk stop, squeeze time, leave a chance to think. Intermittent, the study of deep learning also has a period of time, from the beginning of the small white to now is a primer, halfway to read a little article literature, there are many problems. The trip to Takayama has only ju
be proved that the mutual information of A and C will not exceed the mutual information of A and B. This indicates that information processing does not increase, and most processing loses information. Of course, if the lost is useless information that much good AH), and remained unchanged, which means that the input I through each layer of SI has no information loss, that is, in any layer of SI, it is the original information (that is, input i) another expression. Now back to our topic
Requeststoragepermissionactivity have declaredandroid:taskaffinity="Com.squareup.leakcanary"Properties, which are both said to be running in the new Taskstack, if you are unfamiliar with the taskaffinity attribute, see the article I wrote earlier: Android source series After understanding the relevant configuration of leakcanary, let's look at its related resource files:Resource files are not explained in detail, what we can do for these resources is
习各种人体行为，根据学习结果判断测试视频中的行为类型。本文提出了一种基于深度信念网络（deep belief networks）的人体行为识别方法。1 Behavior Recognition Overall processThe flowchart is as follows:The left branch is the model training, and the right model is the recognition process.2 Foreground extractionAt present, the target detection methods mainly include background subtraction, optical flow method and time differe
This section mainly introduces a deep learning MATLAB version of the Toolbox, Deeplearntoolbox The code in the Toolbox is simple and feels more suitable for learning algorithms. There are common network structures, including deep networks (NN), sparse self-coding networks (SAE), CAE, depth belief networks (DBN) (based
nonlinearity of the network, but also maintain the sensation field of the previous layer, so it has a good effect on the detection of small objects. The original 5x5 convolution kernel is replaced by two 3x3 convolution cores, reducing the parameters, increasing the nonlinearity of the network and the module sensing field. Hypernet:concatenation of Multi-scale Intermediate outputs Hypernet the convolution level of different convolution
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