convolutional neural network example

layer, then the derivative of the cost function to all the parameters of the network can be obtained. This method of calculating gradients is BP.The traditional multilayer neural network is a special example of the above system, where each module is an alternating matrix multiplication (parameter) and element-wise sig

algorithm is specified. In order to achieve good results, the LM training method was used.Save the results. After the training is completed, the result of compression is the value of the hidden layer neuron vector corresponding to each input mode, as well as the weights and thresholds of the network. Save As Mat file using the Save commandSteps:Extract.To load a data file using the load commandInverse Normalization of dataReconstructionInverse normal

Recurrent neural NetworksIn traditional neural networks, the model does not focus on the processing of the last moment, what information can be used for the next moment, and each time will only focus on the current moment of processing. For example, we want to classify the events that occur at every moment in a movie, and if we know the event information in front

The construction of Neural Networks (neural network) is inspired by the operation of biological neural network function. Artificial neural networks are usually optimized by a learning method based on mathematical statistics, so ar

Code address for this section Https://github.com/vic-w/torch-practice/tree/master/rnn-timer RNN full name Recurrent neural network (convolutional neural Networks), which is a memory function by adding loops to the network. The natural language processing, image recognit

regression, and then the parameters are calculated by the gradient descent algorithm.1,error Back propagation algorithm:We know that the gradient descent algorithm consists of two steps:(1), the partial derivative of the parameter theta is obtained for cost function;(2), the parameter theta is updated and adjusted according to the partial derivative;Error Back propagation algorithm provides an efficient method for partial derivative.For example, in t

framework of Neural network is as follows The diagram shows how a single neuron works in a typical neural network, which is explained in detail below.Like the human nervous system, data input is the same as the dendrites that receive stimuli and then the neuron checks and processes the input. Finally, the data is tra

the face have moved to another corner of the image, as shown in Fig. 3:The same number of activations occurs in this example, however they occur in a different region of the green and yellow VO Lumes. Therefore, any activation in the first slice of the yellow volume means that a-face is detected, independently of T He face location. Then the fully-connected layer was responsible to ' translate ' a face and a human body. In both examples, an activatio

not only be one-dimensional, but also can be multidimensional. The section is detailed as follows: BP Neural Network and MATLAB implementation And the train function comes out is the training network Net,matlab out of the net is a structure of data, which includes all the information of the network (train

/1406.2661.gan first Paper:lan Goodfellow generative adversarial Networks 5. Algorithm: Using random gradient descent method to train d,g. Specifically also in the above article. 6.DCGAN Principle Introduction: The best model for image processing applications in deep learning is CNN, how CNN and Gan combine. The answer is Dcgan. The principle is the same as Gan. Just replaced the above G and D with two convolutional

. Build model (Generative): Learning about the federated distribution of the observed data, such as 2-d: P (x, y). Discriminant model: The conditional probability distribution P (y|x) is learned, that is, the distribution of non-observable variables under the premise of observing the variable x.In layman's terms, we want to generate new data by generating models to learn the distribution from the data. For example, learn from a large numb

).name,‘Desktop_1.ini‘)|| strcmp(in_filelist(j).name,‘Desktop_2.ini‘) else tempind=tempind+1; imglist{tempind}=imread(strcat(rootpath,‘/‘,in_filelist(j).name)); end end endendend 2.2 Feature Extraction Extract features from all images, binarization-resize-extract features function feature = feature_lattice(img)% 输入:黑底白字的二值图像。输出：35维的网格特征% ======提取特征，转成5*7的特征矢量,把图像中每10*10的点进行划分相加，进行相加成一个点=====%%======即统计每个小区域中图像象素所占百分比作为特征数据====%for

I ask Xi Xi, a few days ago to play with a bit of MATLAB in the use of Neural network toolbox, and suddenly there is "palpable" the sense of the well-being. The other is nothing, but the data structure of the neural network is a bit "weird", if careless will cause the toolbox error. Here is the correct open posture for

Learning notes TF053: Recurrent Neural Network, TensorFlow Model Zoo, reinforcement learning, deep forest, deep learning art, tf053tensorflow Recurrent Neural Networks. Bytes. Natural language processing (NLP) applies the network model. Unlike feed-forward neural

In the first two sections, the logistic regression and classification algorithms were introduced, and the linear and nonlinear data sets were classified experimentally. Logistic uses a method of summation of vector weights to map, so it is only good for linear classification problem (experiment can be seen), its model is as follows (the detailed introduction can be viewed two times blog: linear and nonlinear experiments on logistic classification of machine learning (continued)): That being the

implication of this is that the statistical characteristics of the part of the image are the same as the rest. This also means that the features we learn in this section can also be used in other parts, so we can use the same learning features for all the locations on this image. More intuitively, when a small piece is randomly selected from a large image, such as 8x8 as a sample, and some features are learned from this small sample, we can apply the feature learned from this 8x8 sample as a de

Article reproduced from: http://www.52analysis.com/R/1627.html Neural Network (optimization algorithm) Artificial neural Network (ANN), referred to as neural network, is a mathematical model or computational model that mimics th

Deep learning Sigmoid neuronsThe Learning algorithm sounds good, but the question is: how do we tailor a learning algorithm for neural networks? Now suppose there is a network of perceptual agencies, and we want to make this network learn how to solve some problems. For example, for a

What is an activation function When biologists study the working mechanism of neurons in the brain, it is found that if a neuron starts working, the neuron is a state of activation, and I think that's probably why a cell in the neural network model is called an activation function.So what is an activation function, and we can begin to understand it from the logistic regression model, the following figure i

schematic diagram of an artificial neural cell, in which X1 ... xn: the input of nerve cells, which is the signal of input neurons. W1 WN: The weight of each input is the same as the thickness and strength of each axon and dendrites in a biological neural network. B: Bias weight threshold: bias (You can view threshold * B as the threshold of biological nerve cel