simple neural network example

,... 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 filter% Numfilters-number of convolution

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

: in the above example, we used two sets of convolution + pooling layer, in fact, these operations can be repeated in a convolutional network countless times. Today, there are a number of outstanding convolutional networks with 10 convolution + pooling layers. Also, not every convolutional layer is followed by a pooled layer. As shown in Figure , we can have a continuous set of convolution +relu layers, fol

) element in a feature graph (or several feature graphs). A shift block (patch) a multiline or column-derived small block (patch) is used as an input to a neighboring pool unit, reducing the number of dimensions represented and creating the invariance of small shifts and deformations (invariance to small shifts and distortions). The convolution of two or three layers, the nonlinearity and the pooling are stacked, followed by the convolution and the fully connected layer. The convnet of the rever

Here is the [1] derivation of the BP algorithm (backpropagation) steps to tidy up, memo Use. [1] the direct use of the matrix differential notation is deduced, the whole process is very concise. And there is a very big advantage of this matrix form is that it is very convenient to implement the programming Control.But its practical scalar calculation deduction also has certain advantages, for example, can clearly know that a weight is affected by who.

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 NetworksThis article will also give an a

example, you is going to generate an image of the Louvre Museum in Paris (content image C), mixed with a painting By Claude Monet, a leader of the Impressionist movement (style image S). Let's see how you can do this. 2-transfer Learning Neural Style Transfer (NST) uses a previously trained convolutional network, and builds on top of. The idea of using a

easy to fit. But the ability to be small, there is no way to model complex functions, that is, to give you data, you can not digest. On the introduction of neural network, here do not say, developed so long, the introduction of neural network books or data too much. Do you remember what we were doing? We want to know

machine learning theory and applications at the University of California, San Diego (UCSD), which explains the basics of convolution networks in plain language and introduces the long Short-term memory (LSTM) model. Given the wide applicability of deep learning in realistic tasks, it has attracted the attention of many technical experts, investors and non-professional professionals. Although the most notable achievement of deep learning is the use of feedforward convolution

., the sum of squared errors (SSE)). Please note that I extend this statement to the whole machine learning continuum, not just the neural network. In the previous article, the common least squares algorithm was used to achieve this, and it found a combination of coefficients that minimized the error squared and the least squares.Our neural

reversal of the convolutional neural network. For example, enter the word "cat" to train the network by comparing the images generated by the network with the real images of the cat, so that the network can produce images more li