First up, this is most certainly homework (so no full code samples please). That said...
I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural... moreFirst up, this is most certainly homework (so no full code samples please). That said...
I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural Network toolbox in Matlab. The data set is the UCI Artificial Characters Database. The problem is, I've had a good tutorial on supervised algorithms, and been left to sink on unsupervised.
So I know how to create a self organising map using selforgmap, and then I train it using train(net, trainingSet). I don't understand what to do next. I know that it's clustered the data I gave it into (hopefully) 10 clusters (one for each letter).
Two questions then:
How can I then label the clusters (given that I have a comparison pattern)?
Am I trying to turn this into a supervised learning problem when I do this?
How can I create a confusion matrix on (another) testing set to compare to the supervised algorithm?
I think I'm missing something conceptual or jargon-based here - all my searches come up with supervised learning... less
Perhaps too general a question, but can anyone explain what would cause a Convolutional Neural Network to... morePerhaps too general a question, but can anyone explain what would cause a Convolutional Neural Network to diverge?
Specifics:
I am using Tensorflow's iris_training model with some of my own data and keep getting
ERROR:tensorflow:Model diverged with loss = NaN.
Traceback...
tensorflow.contrib.learn.python.learn.monitors.NanLossDuringTrainingError: NaN loss during training.
I've tried adjusting the optimizer, using a zero for learning rate, and using no optimizer. Any insights into network layers, data size, etc is appreciated. less
I'm trying to understand the backpropagation algorithm with an XOR neural network as an example. For this case there are 2 input neurons + 1 bias, 2 neurons in the hidden layer +... moreI'm trying to understand the backpropagation algorithm with an XOR neural network as an example. For this case there are 2 input neurons + 1 bias, 2 neurons in the hidden layer + 1 bias, and 1 output neuron.
A B A XOR B 1 1 -1 1 -1 1 -1 1 1 -1 -1 -1
(source: wikimedia.org)
I'm using stochastic backpropagation.
After reading a bit more I have found out that the error of the output unit is propagated to the hidden layers... initially this was confusing, because when you get to the input layer of the neural network, then each neuron gets an error adjustment from both of the neurons in the hidden layer. In particular, the way the error is distributed is difficult to grasp at first.
Step 1 calculate the output for each instance of input.Step 2 calculate the error between the output neuron(s) (in our case there is only one) and the target value(s):Step 3 we use the error from Step 2 to calculate the error for each hidden unit h:
The 'weight kh' is the weight between the hidden unit h and the output... less
From my understanding, Hbase is the Hadoop database and Hive is the data warehouse.
Hive allows to create tables and store data in it, you can also map your existing HBase tables to Hive and operate on them.
why we should use hbase if hive do all that? can we use hive by itself? I'm confused :(
I am trying to collect data from a .txt file and add it into a matrix in Matlab for plotting purposes, but there seems to be an error when collecting the data. It seems to be... moreI am trying to collect data from a .txt file and add it into a matrix in Matlab for plotting purposes, but there seems to be an error when collecting the data. It seems to be happening with the time record.
I am using the following code snippet.
Error using textscan
Unable to parse the format character vector at position 16 ==> %{HH:MM:SS}T %f %f %f %f %f %f %f %d %d %d %f %f %f %f %f %f %f %f %f %f
%f %f %f %f... less
What is the difference between 'SAME' and 'VALID' padding in tf.nn.max_pool of tensorflow?
In my opinion, 'VALID' means there will be no zero padding outside the edges when we... moreWhat is the difference between 'SAME' and 'VALID' padding in tf.nn.max_pool of tensorflow?
In my opinion, 'VALID' means there will be no zero padding outside the edges when we do max pool.
According to A guide to convolution arithmetic for deep learning, it says that there will be no padding in pool operator, i.e. just use 'VALID' of tensorflow. But what is 'SAME' padding of max pool in tensorflow?
is their any possible way to call all the R packages/libraries and functions (packages like raster, rgdal, maptools etc.) in .net framework so that i am able to access all the features of R and run R script using .Net frontend....
i have some data and Y variable is a factor - Good or Bad. I am building a Support vector machine using 'train' method from 'caret' package. Using 'train' function i was able to... morei have some data and Y variable is a factor - Good or Bad. I am building a Support vector machine using 'train' method from 'caret' package. Using 'train' function i was able to finalize values of various tuning parameters and got the final Support vector machine . For the test data i can predict the 'class'. But when i try to predict probabilities for test data, i get below error (for example my model tells me that 1st data point in test data has y='good', but i want to know what is the probability of getting 'good' ...generally in case of support vector machine, model will calculate probability of prediction..if Y variable has 2 outcomes then model will predict probability of each outcome. The outcome which has the maximum probability is considered as the final solution)
**Warning message: In probFunction(method, modelFit, ppUnk) : kernlab class probability calculations failed; returning NAs**
sample code as below
library(caret) trainset <- data.frame( class=factor(c("Good", "Bad", "Good",... less
Right now I'm importing a fairly large CSV as a dataframe every time I run the script. Is there a good solution for keeping that dataframe constantly available in between runs... moreRight now I'm importing a fairly large CSV as a dataframe every time I run the script. Is there a good solution for keeping that dataframe constantly available in between runs so I don't have to spend all that time waiting for the script to run?
I'm working on machine learning problem and want to use linear regression as learning algorithm. I have implemented 2 different methods to find parameters theta of linear... moreI'm working on machine learning problem and want to use linear regression as learning algorithm. I have implemented 2 different methods to find parameters theta of linear regression model: Gradient (steepest) descent and Normal equation. On the same data they should both give approximately equal theta vector. However they do not.
Both theta vectors are very similar on all elements but the first one. That is the one used to multiply vector of all 1 added to the data.
Here is how the thetas look like (fist column is output of Gradient descent, second output of Normal equation):
Grad desc Norm eq -237.7752 -4.6736 -5.8471 -5.8467 9.9174 9.9178 2.1135 2.1134 -1.5001 -1.5003 -37.8558 -37.8505 -1.1024 -1.1116 -19.2969 -19.2956 66.6423 66.6447 297.3666 296.7604 -741.9281 -744.1541 296.4649 296.3494 146.0304 144.4158 -2.9978 -2.9976 -0.8190 -0.8189
What can cause the difference in theta(1, 1) returned by gradient descent compared to theta(1, 1) returned by normal equation? Do I have bug in my... less
How to save/restore a model after training? (26... moreThis question already has answers here:
How to save/restore a model after training? (26 answers)
Closed 3 years ago.
I'm relatively new to machine learning and the Tensorflow framework. I was trying to take my trained model heavily influenced by the code presented here, using the MNIST handwritten digit dataset and perform inferences on testing examples that I have created. However, I am doing the training on a remote machine with a GPU and am trying to save the data to a directory so that I can transfer the data and inference on a local machine
It seems that I was able to save some of the model with tf.saved_model.simple_save, however, I'm unsure of how to use the saved data to do inferencing and to use the data to make a prediction given a new image. It seems like there are multiple ways to save a model, but I am unsure of what the convention or of what the "correct way" is to do it with the Tensorflow framwork.
So far, this is the line that I think I would need, but am unsure if it is... less