cross-validation

I've been using tensorflow for a while now. At first I had stuff like this: def myModel(training): with tf.scope_variables('model', reuse=not training): do model return model training_model = myModel(True) validation_model = myModel(False) Mostly because I started with some MOOCs that tought me to do that. But they also didn't use TFRecords or Queues. And I didn't know why I was using two separate models. I tried building only one and feeding the data with the feed_dict : everything worked. Ever since I've been usually using only one model. My inputs are always place_holders and I just input

I have a frequency table of data in a data.frame in R listing factor levels and counts of successes and failures. I would like to turn it from frequency table into a list of events - i.e. the opposite of the "table" command. Specifically, I would like to turn this: factor.A factor.B success.count fail.count -------- -------- ------------- ---------- 0 1 0 2 1 1 2 1 into this: factor.A factor.B result -------- -------- ------- 0 1 0 0 1 0 1 1 1 1 1 1 1 1 0 It seems to me that reshape ought to do this, or even some obscure base function that I have not heard of, but I've had no luck. Even

So I've read a paper that had used neural networks to model out a dataset which is similar to a dataset I'm currently using. I have 160 descriptor variables that I want to model out for 160 cases (regression modelling). The paper I read used the following parameters:- 'For each split, a model was developed for each of the 10 individual train-test folds. A three layer back-propagation net with 33 input neurons and 16 hidden neurons was used with online weight updates, 0.25 learning rate, and 0.9 momentum. For each fold, learning was conducted from a total of 50 different random initial weight

I need to keep track of the F1-scores while tuning C & Sigma in SVM, For example the following code keeps track of the Accuracy, I need to change it to F1-Score but I was not able to do that……. %# read some training data [labels,data] = libsvmread('./heart_scale'); %# grid of parameters folds = 5; [C,gamma] = meshgrid(-5:2:15, -15:2:3); %# grid search, and cross-validation cv_acc = zeros(numel(C),1); for i=1:numel(C) cv_acc(i) = svmtrain(labels, data, ... sprintf('-c %f -g %f -v %d', 2^C(i), 2^gamma(i), folds)); end %# pair (C,gamma) with best accuracy [~,idx] = max(cv_acc); %# now you can