convolutional neural network tensorflow

After figuring out the fundamentals of convolutional Neural Networks (CNN), in this post we will discuss the algorithm implementation techniques based on Theano. We will also use mnist handwritten numeral recognition as an example to create a convolutional neural network (CN

1. Target positioning 1.1 Introduction to classification, positioning and testing -Image classificationImage classification, is to give you a picture, you determine the target category, such as cars, cats and so on.-Classification with localizationPositioning classification, not only to determine the target category, but also to output the position of the target object, such as the box up.-DetectionDetection, there may be multiple objects in the picture, you need to find them out. 1.2 Position

TensorFlow implements RNN Recurrent Neural Network, tensorflowrnn RNN (recurrent neural Network) recurrent neural Network It is mainly used for natural language processing (NLP) RNN is

CNN (convolutional neural Network)Convolutional Neural Networks (CNN) dating back to the the 1960s, Hubel and others through the study of the cat's visual cortex cells show that the brain's access to information from the outside world is stimulated by a multi-layered recepti

the local feature is extracted, the position relationship between it and other features is determined; s layer is the feature map layer, and each computing layer of the network is composed of multiple feature mappings. Each feature is mapped to a plane, and the weights of all neurons on the plane are equal. The feature mapping structure uses the sigmoid function which affects the function core as the activation function of

This note describes the third week of convolutional neural networks: Target detection (1) Basic object detection algorithmThe main contents are:1. Target positioning2. Feature Point detection3. Target detectionTarget positioningUse the algorithm to determine whether the image is the target object, if you want to also mark the picture of its position and use the border marked outAmong the problems we have st

that the 2D convolution is actually 3D (the dimensions of the convolution kernel should be kernel_height * kernel_height * input_channel), except that the third dimension is exactly equal to the number of input channels, So the volume after the third dimension on the lost, became a flat two-dimensional feature map, so called 2D convolution.Another way of understanding is that the shape of a convolution core is kernel_height * kernel_height, and there is a input_channel layer, the process of mak

The first part of the full-connected network weights updateconvolutional neural network using gradient-based learning methods to supervise training, in practice, the general use of random gradient descent (machine learning in several common gradient descent) version, for each training sample is updated once the weight, error function using the error square Sum fu

In this blog post we briefly analyze the class--layers of the last network structure in the TINY_CNN convolutional neural network model.First of all, layers can be called a layer structure of the vector, that is, the layer structure of the container. Because convolutional

AlexNet: (ILSVRC Top 5 test error rate of 15.4%) the first successful display of the convolutional neural network potential network structure. key point: with a large amount of data and long-time training to get the final model, the results are very significant (get 2012 classification first) using two GPU, divide

Original articleReprint please register source HTTP://BLOG.CSDN.NET/TOSTQ the previous section we introduce the forward propagation process of convolutional neural networks, this section focuses on the reverse propagation process, which reflects the learning and training process of neural networks. Error back propagation method is the basis of

Turn from: The Heart of the machine Introduction Frankly speaking, I can't really understand deep learning for a while. I look at relevant research papers and articles and feel that deep learning is extremely complex. I try to understand neural networks and their variants, but still feel difficult. Then one day, I decided to start with a step-by-step basis. I break down the steps of technical operations and manually perform these steps (and calcula

Study, the use of convolutional neural network has been a long time, the period has been based on the Caffe framework of the Jiayanqing great God to study other people's model, or in the boring time in the same way as the fortune-telling, eyes micro-closed, bobbing, the mouth occasionally leaking a few syllables, a long time DIY out of a think of a lot of models,

model, which uses the class (cifar10.py and cifar10_input.py) to read CTFAR-10 data (CTFAR-10 a classic dataset) when building models git clone git@github.com:tensorflow/models.git Convolution neural network structure:CONV1 convolution layer and activation functionMaximum pool of pool1Norm1 LRNConv2 convolution layer and activation functionNorm2 LRNMaximum pool layer of pool2LOCAL3 full join layer and ac

convolutional Neural Networks (convolution neural network, CNN) have achieved great success in the field of digital image processing, which has sparked a frenzy of deep learning in the field of natural language processing (Natural Language processing, NLP). Since 2015, papers on deep learning in the field of NLP have e

Kalchbrenner ' s PaperKal's article cited a high number of citations, he proposed a network model called DCNN (Dynamic convolutional neural Networks), in the previous (Kim's Paper) experimental results Section also verified the effectiveness of this model. The subtleties of this model lie in the way of pooling, using a method 动态Pooling called.Is the model of th

biased term, followed by a nonlinear function. If you use $h ^{k}$ to represent the feature map of the $k $ layer, the corresponding filter is determined by the $W ^{k}$ and bias $b _{k}$, then the feature map $h ^{k}$ can be computed from the next (using Tanh for nonlinear functions):$h _{ij}^{k}=tanh (w^{k}*x) _{ij}+b_{k}$In order to get a richer representation of the data, each hidden layer is usually composed of multiple feature graphs: $\{h^{\text{(k)}},k=0,... k\}$. The weight $W $ is rep

Refer to:Https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0(The fall of Rnn/lstm)"hierarchical neural attention encoder", shown in the figure below:Hierarchical neural Attention EncoderA better-to-look-into-the-past is-to-use attention modules-summarize all past encoded vectors into a context vector Ct.Notice There is a hierarchy of attention modules here, very similar to the hierarchy of

C ++ convolutional neural network example: tiny_cnn code explanation (10) -- layer_base and layer Class Structure Analysis In the previous blog posts, we have analyzed most of the layer structure classes. In this blog post, we plan to address the last two layers, it is also the two basic classes layer_base and layer that are at the bottom of the hierarchy for a b