I am looking for guidance to generate multiple classifier training data from document. e.g. if particular document has three sections with each 10 pages in each sections. (total 30 pages) I am looking for open source library, where I can pass on document (explicitly specifying section 1, section 2 and section 3 pages) then it… Read More generating multi classifier training data from document
this is a sentiment analysis model made with tf-idf for feature extraction i want to know how can i save this model and reuse it. i tried saving it this way but when i load it , do same pre-processing on the test text and fit_transform on it it gave an error that the model… Read More Keep model made with TFIDF for predicting new content using Scikit for Python
My loss function for a DNN classification task is eigenvalue-based which does not need the inputs Y_prediction and Y_actual. Is it possible to write specialized custom loss functions like that using Tensorflow? >Solution : Of course: def customLoss(y_true, y_pred, alpha): loss = ….alpha return loss model.compile(loss=customLoss(alpha), optimizer=’sgd’)
I am training a convolutional neural network for binary time series classification. The training accuracy on both models is very different. If on the first it grows, then on the second it is always about 40%. There were also strong "jumps". Change filters=32/64/128 and epochs didn’t give the best results(kernel_size = 8). self.model = keras.Sequential([… Read More Deep learning CNN: low accuracy
I am trying to figure out how to plot the decision boundary line picking just few middle values instead of the entirety of the separator line such that it spans roughly the y-range that also the observations span. Currently, I manually repeatedly select different bounds and assess visually, until "a good looking separator" emerged. MWE:… Read More Plotting class decision boundary: determine a "good fit" range directly
I’m wondering if there is a more dense way to do the following, which is essentially splitting column-separated data, by row and into one of three categories depending on the final entry of the row: xi_test_0 = [xi_test_sc[i] for i in range(len(xi_test_sc)) if y_test[i] == 0] xii_test_0 = [xii_test_sc[i] for i in range(len(xii_test_sc)) if y_test[i]… Read More Is there a more dense method to conditionally separate data?
I have been trying to create a TensorFlow model that can project similarity % between two images. I tried to use a normal CNN with binary classes but I have been unable to obtain a model with good accuracy. I have come to the conclusion that I require a model that takes in multiple inputs… Read More ML model that projects multiple similarities