training-data

问题 I want to replicate this tutorial to classify two groups https://machinelearningmastery.com/develop-n-gram-multichannel-convolutional-neural-network-sentiment-analysis/ with different dataset but could not do that despite being hardly trying. I am new to programming so would appreciate any assistance or tips that could help. My dataset is small (240 files for each group), and files named 01 - 0240. It is around these lines of codes, I think. if is_trian and filename.startswith('cv9'):

问题 I want to make a image classifier, but I don't know python. Tensorflow.js works with javascript, which I am familiar with. Can models be trained with it and what would be the steps to do so? Frankly I have no clue where to start. The only thing I figured out is how to load "mobilenet", which apparently is a set of pre-trained models, and classify images with it: const tf = require('@tensorflow/tfjs'), mobilenet = require('@tensorflow-models/mobilenet'), tfnode = require('@tensorflow/tfjs-node

问题 I want to make a image classifier, but I don't know python. Tensorflow.js works with javascript, which I am familiar with. Can models be trained with it and what would be the steps to do so? Frankly I have no clue where to start. The only thing I figured out is how to load "mobilenet", which apparently is a set of pre-trained models, and classify images with it: const tf = require('@tensorflow/tfjs'), mobilenet = require('@tensorflow-models/mobilenet'), tfnode = require('@tensorflow/tfjs-node

来源： https://stackoverflow.com/questions/34462312/is-there-any-technique-to-know-in-advance-the-amount-of-training-examples-you-ne

来源： https://stackoverflow.com/questions/62662194/keras-imagedatagenerator-validation-split-not-selected-from-shuffled-dataset

来源： https://stackoverflow.com/questions/62958248/calculate-accuracy-for-each-class-using-cnn-and-pytorch

来源： https://stackoverflow.com/questions/62958248/calculate-accuracy-for-each-class-using-cnn-and-pytorch

问题 I am using gensim to create Word2Vec models trained on large text corpora. I have some models based on StackExchange data dumps. I also have a model trained on a corpus derived from English Wikipedia. Assume that a vocabulary term is in both models, and that the models were created with the same parameters to Word2Vec. Is there any way to combine or add the vectors from the two separate models to create a single new model that has the same word vectors that would have resulted if I had

问题 The --passes flag is the number of training passes. But it's unclear what the notion of passes means when training a Python VW model, within a for loop for example. e.g. If I'm training a model example by example within a for-loop like this: for line in train: model.learn(line) How could there be multiple passes if each training sample is learned from only once? 回答1: In Python the passes option only affect when the inbuilt driver is used. This only occurs when a data file and or passes is

问题 The --passes flag is the number of training passes. But it's unclear what the notion of passes means when training a Python VW model, within a for loop for example. e.g. If I'm training a model example by example within a for-loop like this: for line in train: model.learn(line) How could there be multiple passes if each training sample is learned from only once? 回答1: In Python the passes option only affect when the inbuilt driver is used. This only occurs when a data file and or passes is