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Questions tagged [training]

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What research exists to help design training data sets where contamination due to privacy is an issue?

Suppose one is building a classifier that: takes as input the e-mail body text returns true or false if person X should be included in the address list To build this classifier we have historical ...
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1answer
12 views

Model biased towards low frequency data?

Generally model gets biased towards data_samples/target whose frequency is high in training data set. Is it possible during training that model gets biased towards low frequency training data set.
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The principle and understanding of adversarial training

In the paper《ADVERSARIAL TRAINING METHODS FOR SEMI-SUPERVISED TEXT CLASSIFICATION》and its related papers. The researcher apply the adversarial perturbation to word embeddings. Why do this methods ...
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1answer
16 views

Am I using GridSearch correctly or do I need to use all data for cross validation?

I'm working with a dataset that has 400 observations, 34 features and quite a few outliers, some of them extreme. Given the nature of my data, these need to be in the model. I started by doing a 75-...
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1answer
9 views

When to enhance a dataset of images with flips and rotations of the images?

I am a beginner in machine learning, so I'm sorry if my question is a bit trivial. Suppose I have a dataset of images and which I want to classify, say using a neural network. It makes sense to me to ...
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15 views

Difference between retraining on different parts of the data and training initially on larger data set

I have a large data set that doesn't fit in memory and would have to use something like Keras's model.fit_generator if I would like to train the model on all of the ...
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22 views

Keras functional API multi-input size error

I'm trying to define a multi-input model on a list of different arrays, all with the same shape (n_points, size, size, 1). I defined this model using the ...
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0answers
10 views

Using fastai library to get data from Google

So I was doing the fastai online course and I have a doubt in lecture 2 (link for the code given below). First of all, when we are using Google to generate the dataset, where have we ensured that the ...
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1answer
72 views

Training Keras model with multiple CSV files

I'm currently trying to train a Keras model on several large CSV files. I can fit one in memory, but not all combined. From my point of view, there are several ways to deal with this problem. I could ...
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2answers
46 views

Split the data between the Training Data and Test Data using sklearn

Work to do My job is to take the data and divide it between Training and Test using 30% of the data as Test where both should have the same ratio between positive and negative. CSV File ...
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Very Fast Training After First Epoch

I trained an InceptionV3 model using plant images. I used Keras library. When training was started, first epoch took 29s per step and then other steps took approximately 530ms per step. So that made ...
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Training loss and accuracy is oscillating and failed to converge

I was using AlexNet to do dog&cat classification tasks practice: https://github.com/stephen-v/tensorflow_alexnet_classify While I run the training, the loss oscillated while decreasing, which ...
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1answer
44 views

Training a model where each response in the observation data has a different known varience

I have a dataset where each response variable is the number of successes of N Bernoulli trials with N and p (the probability of success) being different for each observation. The goal is to train a ...
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1answer
37 views

What happens to the left over unpicked data in Random Forest

I believe in Random forest we pick random samples of training data with replacement. My question is there still is a possibility that we might leave some data out. What happens to that. Does it not ...
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1answer
17 views

Difference - Batch Training or training multiple times?

I have a question on batch learning of neural network. A neural network learns in batches and modifies weights in every iteration. Question: If I save checkpoints after a batch, and then load the ...
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1answer
13 views

Running multiple times of a model is for model randomness or data randomness?

When a paper report the average and std of a model on a dataset, it means that they have changed the split of training and test sets and run the model multiple times or they just run the model on ...
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2answers
17 views

Given a single discrete data set, how should I divide it into training data and test data?

I have a dataset in libSVM format consisting of 6000 entries, each with 5 indices, and each index has a binary value 1 or 2. Each of the 6000 entries has a label of ...
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0answers
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How to do channel-wise incremental training of Deep Neural Network in Caffe framework?

I read the idea of channel-wise incremental training from the paper: Runtime configurable deep neural networks for energy-accuracy trade-off (https://dl.acm.org/citation.cfm?id=2968458). The idea is ...
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2answers
147 views

Why real-world output of my classifier has similar label ratio to training data?

I trained a neural network on balanced dataset, and it has good accuracy ~85%. But in real world positives appear in about 10% of the cases or less. When I test network on set with real world ...
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Training data : forecasted or actual?

I am working on a time series prediction problem. I am using keras models for machine learning. For this prediction, weather variables are used as input. They can be of two types: forecasted and ...
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2answers
34 views

oversampling data with subclass

Oversampling of under-represented data is a way to combat class imbalance. For example, if we have a training data set with 100 data points of class A and 1000 data points of class B, we can over ...
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0answers
14 views

Speeding up actor critic training

I'm simulating a very simple system, recommendation system, and I am running an actor-critic model to predict what item I should recommend next. The agent is learning and is doing just fine. However, ...
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0answers
27 views

Replacing mean by median over batch-size to lessen the impact of outliers

In the case of training a Neural Network on a regression task. Assuming the data has a significant amount of outliers. Provided that the error needs to be RMS and not MAE. Can it be better (as in less ...
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Should images with multiple objects of the same class be used as training sample for multi-classes object detection models?

Let's say the model try to detect all the grapes on a branch of grapes. Can I use images of a grape branch with all the grapes labeled as a training sample? Will it affect the quality of the RPN ? Is ...
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2answers
61 views

Why can decision trees have a high amount of variance

I've heard that decision trees can have a high amount of variance, and that for a data set $D$ split into test/train the decision tree could be quite different depending on how the data was split. ...
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1answer
135 views

Over fitting in Transfer Learning with small dataset

I am using Transfer Learning to perform image classification. Base model used : Resnet50 using ImageNet dataset ...
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1answer
24 views

Validation data shall be in broken down into batches or not?

I am using fit_generator to train the model. The training dataset is being read from a generator function which gives data in a constant batch size. Now I want to ...
2
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1answer
129 views

Incrementally Train BERT with minimum QnA records - to get improved results

We are using Google BERT for Question and Answering. We have fine tuned BERT with SQUAD QnA release train data set (https://github.com/google-research/bert , https://rajpurkar.github.io/SQuAD-explorer/...
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1answer
43 views

review: gradient descent, epochs, validation in neural network training

These days, training data aren't put in gradient descent all at once. Rather, they are put in batch after batch. Gradient descent is run once for each batch of training data. When all batches are ...
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1answer
105 views

Using SMOTE for Synthetic Data generation to improve performance on unbalanced data

I presently have a dataset with 21392 samples, of which, 16948 belong to the majority class (class A) and the remaining 4444 belong to the minority class (class B). I am presently using SMOTE (...
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1answer
41 views

Accuracy and Loss in MLP

I am trying to explore models for predicting whether the a team will win or lose based on features about the team and their opponent. My training data is 15k samples with 760 numerical features. Each ...
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0answers
40 views

What to do when Kfold is not enough?

I have a dataset made of roughly 100 time-series and my final goal is to obtain a classification of each point (detection problem). To do so I have labels so I decided to use an XGB model to perform ...
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0answers
19 views

How to train non image data in batches from disk?

I am working on a project where I have 50 .npy files with each of shape (77156, 30, 50, 1) representing ...
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1answer
15 views

Change rate of cross validation data, after training

Say we have N of labeled data, and we need to take some part for the cross validation (we will skip test part for this case). We ...
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0answers
41 views

FaceNet training, tripletloss not decrease but accuracy increase then stuck,what are possible causes?

as you can see,triplet loss(pink curve in the left) do not change,but accuracy increase then stuck,what are possible reasons?
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1answer
111 views

Issues with training SSD on own dataset

I'm new to ML and trying to train a SSD300, with some Keras-Code github.com/pierluigiferrari/ssd_keras I found on github. For training I'm using an own (very small) dataset of objects that are not in ...
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1answer
32 views

Is “adding the predictions to the real data for new training and prediction” a good idea for LSTM?

Considering we have trained our model with a lot of data for "many-to-one" prediction. Then we like to forecast the future data of next 10 days. So we use last 60 of existent data and predict the ...
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0answers
15 views

Performance diagnostics in mxnet gluon (e.g. plotting training vs validation loss over time)?

Tensorflow has tensorboard, is there any recommended way to plot classification error/loss over time in mxnet?
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1answer
39 views

Is splitting the data set into train and validation applicable in unsupervised learning?

I am having a tough time implementing all the steps of setting up support vector machine (SVM) for unsupervised learning. My data set is labelled but for educational purposes I am learning ...
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1answer
28 views

Suggestions for labeling data for named entity recognition [closed]

Is it good to label the data based on sub category than parent category? For example: for drugs data ... label the drugs dose as drug_dose or label the drug dose as different type of dose like ...
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2answers
61 views

Is it ok to train the model only on the interested part of the data?

Let's say I have a dataset where one feature is 'Car type' : say 'A', 'B' and 'C'. The test set consists of samples where 'Car type' is always equal to 'A'. Therefore, should I train my model only on ...
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47 views

Validation curve

I'm learning about data science and I've been checking several tutorials. Now I'm trying some validation curves on the problem sample I'm resolving and I'm having some troubles with it. This is the ...
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0answers
72 views

Training deep CNN with noisy dataset

I am training a Mask RCNN model with a train dataset that has been generated from some simple computer vision operations (color thresholding) and some morphological filtering. The train set captures ...
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4answers
120 views

Weights not converging while cost function has converged in neural networks

My cost/loss function drops drastically and approaches 0, which looks a sign of convergence. But the weights are still changing in a visible way, a lot faster than the cost function. Should I ensure ...
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3answers
46 views

weight training speed too slow in CNNs

I'm writing my own CNN code from scratch. Though I got fast, converged and satisfactory results, the trained weights change very little in value (while cost/loss function drops in time rapidly in a ...
2
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1answer
186 views

Why is performance worse when my time-series data is not shuffled prior to a train/test split vs. when it is shuffled prior to the split?

We are running RandomForest model on a time-series data. The model is run in real time and is refit every time a new row is added. Since it is a timeseries data, we set shuffle to false while ...
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1answer
26 views

Computing number of batches in one epoch

I have been reading through Stanford's code examples for their Deep Learning course, and I see that they have computed ...
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0answers
18 views

How to train LSTM with previous cell's prediction as an input in Keras?

At the moment I'm using a simple Keras model to learn a sequence of items and after it using the trained model to generate new sequences . I want to change the training to be in the same manner as ...
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1answer
36 views

Unsupervised Learning and Training Data

As far as I know, we need to use training data to find out the relation between the features, also known as input values, and labels, that are output values, in supervised learning. After that, by ...
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20 views

Train and Test Error dependence on size of data

I was reading about Adaboost Alogorithm and learned that if initially we split the dataset equally for training and testing, apply the algorithm. And then gradually start increasing the size of train ...