simple neural network example

at the whole NIN network below:Look at the first Nin, originally 11*11*3*96 (11*11 convolution kernel, output map 96) for a patch output 96 points, is the output feature map the same pixel 96 channel, but now add a layer of MLP, Make a full connection to these 96 points, and output 96 points-- very ingenious, this new MLP layer is equivalent to a 1 * 1 convolution layer , so in the neural

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

Recently in the study of Artificial neural network (Artificial neural netwroks), make notes, organize ideas Discrete single output perceptron algorithm, the legendary MP Two-valued Network: The value of the independent variable and its function, the value of the vector component only takes 0 and 1 functions, vectors

layers are followed by aThe pooling layer, followed by an all-connected layer arrangement is very common. Layers (layer) The activated dimension (Activation Shape) Size after active (Activation size) Parameters W, b (Parameters) Input (32,32,1) 1024 0 CONV1 (F=5,s=1) (28,28,6) 4704 (5*5+1) *6=156 POOL1 (14,14,6) 1176 0 CONV2 (F=5,s=1) (10,10,16) 1600 (

The previous section in"machine learning from logistic to neural network algorithm", we have introduced the origin and construction of neural network algorithm from the principle, and programmed the simple neural

in general the price and the size of the house are positively correlated. In this case, the relationship of the known data can be represented in a planar coordinate system: The data is linearly fitted, and the house price is never negative, getting the relu function (rectified Linear unit, correcting the linear element)in the graph. In this simple example, the size of the house as input, the price as th

you still return and classify the model, the time required to learn the parameters will be unbearable;II: Neural network-representation1,neural Network ModelIn a neural network, we call the first layer the input layer, and the la

figure, that is, to reduce the interference caused by the difference in the value range of data in each dimension. For example, we have two dimensions: feature a and Feature B, the range of A is 0 to 10, and the range of B is 0 to 10000. If you directly use these two features, there is a problem. A good way is to normalize them, that is, the data of A and B is in the range of 0 to 1.? PCA/whitening: Dimensionality Reduction Using PCA; whitening is th

First, prefaceAfter a period of accumulation, for the neural network, has basically mastered the Perceptron, BP algorithm and its improvement, Adaline and so on the most simple and basic knowledge of feedforward neural network, the following is based on the feedback

is ); The last output node computes sin using a separate input from the addition node.A special attribute of this flow graph is depth (depth): the length of the longest path from one input to one output.The traditional Feedforward neural network can be seen as having a depth equal to the number of layers (for example, the output layer is an implicit layer

://www.cs.toronto.edu /~ Graves/preprinthistory. The development of recurrent neural networks. VanillaRNN-> Enhanced the hidden layer function-> Simple RNN-> GRU-> LSTM-> CW-RNN-> Bidirectional deepening Network-> Bidirectional RNN-> Keep Bidrectional RNN-> Combination of the two: DBLSTMRecurrent Neural Networks, Part

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

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