neural-network

问题 I have created a simple neural network (Python, Theano) to estimate a persons age based on their spending history from a selection of different stores. Unfortunately, it is not particularly accurate. The accuracy might be hurt by the fact that the network has no knowledge of ordinality. For the network there is no relationship between the age classifications. It is currently selecting the age with the highest probability from the softmax output layer. I have considered changing the output

问题 I'm new to machine learning and neural networks. I know how to build a nonlinear classification model, but my current problem has a continuous output. I've been searching for information on neural network regression, but all I encounter is information on linear regression - nothing about nonlinear cases. Which is odd, because why would someone use neural networks to solve a simple linear regression anyway? Isn't that like killing a fly with a nuclear bomb? So my question is this: what makes a

问题 I'm working on creating a 2 layer neural network with back-propagation. The NN is supposed to get its data from a 20001x17 vector that holds following information in each row: -The first 16 cells hold integers ranging from 0 to 15 which act as variables to help us determine which one of the 26 letters of the alphabet we mean to express when seeing those variables. For example a series of 16 values as follows are meant to represent the letter A: [2 8 4 5 2 7 5 3 1 6 0 8 2 7 2 7]. -The 17th

问题 I'm working on creating a 2 layer neural network with back-propagation. The NN is supposed to get its data from a 20001x17 vector that holds following information in each row: -The first 16 cells hold integers ranging from 0 to 15 which act as variables to help us determine which one of the 26 letters of the alphabet we mean to express when seeing those variables. For example a series of 16 values as follows are meant to represent the letter A: [2 8 4 5 2 7 5 3 1 6 0 8 2 7 2 7]. -The 17th

问题 I'm trying to do some text analysis to determine if a given string is... talking about politics. I'm thinking I could create a neural network where the input is either a string or a list of words (ordering might matter?) and the output is whether the string is about politics. However the brain.js library only takes inputs of a number between 0 and 1 or an array of numbers between 0 and 1. How can I coerce my data in such a way that I can achieve the task? 回答1: new brain.recurrent.LSTM(); this

问题 I am trying to compute the derivative of the activation function for softmax. I found this : https://math.stackexchange.com/questions/945871/derivative-of-softmax-loss-function nobody seems to give the proper derivation for how we would get the answers for i=j and i!= j. Could someone please explain this! I am confused with the derivatives when a summation is involved as in the denominator for the softmax activation function. 回答1: The derivative of a sum is the sum of the derivatives, ie: d

问题 I am trying to compute the derivative of the activation function for softmax. I found this : https://math.stackexchange.com/questions/945871/derivative-of-softmax-loss-function nobody seems to give the proper derivation for how we would get the answers for i=j and i!= j. Could someone please explain this! I am confused with the derivatives when a summation is involved as in the denominator for the softmax activation function. 回答1: The derivative of a sum is the sum of the derivatives, ie: d

问题 If you use the GUI nnstart, you provide only input and output, but you can't decide which rows are going to be training, validation and test set because they are chosen randomly. How can they be specified manually? 回答1: You can use any of the GUI's launched from NNSTART to create some sample training code. Then customize that code by setting the following data division values: net.divideFcn = 'divideind'; % Divide data by indices (i.e. not randomly) net.divideParam.trainInd = [... training

问题 Yes, I'm aware that speech recognition is fairly complicated (as an understatement). What I'm looking for is a method for distinguishing between maybe 20-30 phrases. An ability to split words (discrete speech is fine) would be nice, but isn't required. The software will be user-dependent(i.e. for use by me). I'm not looking for existing software, but for a good way of going about doing this myself. I've looked into various existing methods and it seems like splitting the sound into phonemes,

问题 I'm using a neural network made of 4 input neurons, 1 hidden layer made of 20 neurons and a 7 neuron output layer. I'm trying to train it for a bcd to 7 segment algorithm. My data is normalized 0 is -1 and 1 is 1. When the output error evaluation happens, the neuron saturates wrong. If the desired output is 1 and the real output is -1 , the error is 1-(-1)= 2 . When I multiply it by the derivative of the activation function error*(1-output)*(1+output) , the error becomes almost 0 Because of 2