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should focus on. It also reduces the parameters of the neural network.
parameter Sharing (parameter sharing): The parameters of the filter in the same convolutional layer are shared, and a filter in the filter matrix is the same regardless of the location of the convolution operation. (Of course, the same layer different filter parameters, different layers be

calculation, the result is the same.In this example, there are differences in the results, indicating that there must be random components in the system.The random parts of machine learning are usually as follows: 1. The disorderly sequence operation of the training sample; 2. Random gradient descent; 3. The model randomly assigns the initial value.In this example, there is one more: the initial input of t

between the filter parameters are not the same.) Sharing the parameters of the filter allows the content in the image to be unaffected by the position. Take mnist handwritten numeral recognition as an example, whether the number "1" appears in the upper left or bottom right corner, the type of picture is unchanged. Sharing the parameters of the convolution filter can also drastically reduce the parameters on the

are two functions head () and tail (), the implementation mechanism is very simple, I believe you can understand:As for how to access the specified layer, TINY_CNN provides two means, one is to define the at function and type conversion through dynamic_cast:Another method is to overload the "[]" operation, and to access the array as a classThe above two methods of access are indexed (index) to complete, more convenient.OK, about the layer structure container layers class source first introduced

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 th

Transfer from http://blog.csdn.net/zouxy09/article/details/8781543CNNs is the first learning algorithm to truly successfully train a multi-layered network structure. It uses spatial relationships to reduce the number of parameters that need to be learned to improve the training performance of the general Feedforward BP algorithm. In CNN, a small part of the image (local sensing area) as the lowest layer of the input of the hierarchy, the information i

the stratum of BP network is no longer full connection, it is locally connected . This is the simplest one-dimensional convolutional network. If we extend this idea to two-dimensional, this is the convolutional neural network we

C ++ convolutional neural network example: tiny_cnn code explanation (9) -- partial_connected_layer Structure Analysis (bottom)
In the previous blog, we focused on analyzing the structure of the member variables of the partial_connected_layer class. In this blog, we will continue to give a brief introduction to other m

,... filterdim,numfilt ers,pooldim,pred)% calcualte cost and gradient for a single layer convolutional% neural network followed by a Softmax Laye R with cross entropy% objective.%% parameters:% theta-unrolled parameter vector% ima Ges-stores images in Imagedim x Imagedim x numimges% array% Numclasses-number of classes to pred ict% Filterdim-dimension of

convolutional neural network for CNN. The C-layer represents all the layers that are obtained after filtering the input image, also called "convolution layer". The S layer represents the layer that the input image is sampled (subsampling) to get. Where C1 and C3 are convolution layers, S2 and S4 are the next sampling layers. Each layer in the C, S layer consists

output.Displays the size of the resulting output image with a 3x3 grid on the 28x28 image using different step sizes and fill methods:The following is an understanding of the convolution process with two dynamic graphs:The first is a convolution process that is effectively populated with a 3x3 grid on a 5x5 image:The second is the convolution process with the same padding on the 5x5 image using a 3x3 grid, moving in the following way: http://cs231n.github.io/

I've been focusing on CNN implementations for a while, looking at Caffe's code and Convnet2 's code. At present, the content of the single-machine multi-card is more interested, so pay special attention to Convnet2 about MULTI-GPU support.where Cuda-convnet2 's project address is published in: Google Code:cuda-convnet2A more important paper on MULTI-GPU is: one weird trick for parallelizing convolutional neural

example of CNN, shown in Figure 1, to talk about the process of image processing:,After the image input network, the convolution is obtained through three filters (filter) to obtain three feature maps of the C1 layer (feature map). The three feature graphs of the C1 layer are respectively sampled to obtain three feature graphs of the S2 layer. These three feature graphs get three feature graphs of the C3 l

Part five The second model: convolutional neural NetworksDemonstrates the convolution operationLeNet-5-type convolutional neural network is the core of the great breakthrough in the field of computer vision recently. The convolution layer differs from the previous fully conn

,In the above formula, the * number is the convolution operation, the kernel function k is rotated 180 degrees and then the error term is related to the operation, and then summed.Finally, we study how to calculate the partial derivative of the kernel function connected with the convolution layer after obtaining the error terms of each layer, and the formula is as follows.The partial derivative of the kernel function can be obtained when the error item of the convolution layer is rotated 180 deg

Network Steps to do: (a Chinese, teach Chinese, why write a bunch of English?) ）1, sample Abatch of data (sampling)2,it through the graph, get loss (forward propagation, get loss value)3,backprop to calculate the geadiets (reverse propagation calculation gradient)4,update the paramenters using the gradient (using gradient update parameters)What convolutional neural

the matrix range default to 0. This makes it possible to filter each element of the input matrix and output a matrix of the same size or larger. The complement 0 method is also called the wide convolution, the method that does not use the complement zero is called the narrow convolution. Example of 1D:Narrow convolution vs wide convolution. The filter length is 5 and the input length is 7. Source: A convolutional

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