Questions tagged [training]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
2
votes
0answers
143 views

How to resolve the instability of average reward per episode in training of DQN (Deep Q-Network)?

what is shown when average reward per episode in training is unstable? If there is big difference between average reward per episode and final reward by test section, what we can say? For ...
3
votes
1answer
381 views

Why does exploration in DQN not lead to instability?

Why does action exploration in DQN not lead to instability? I see in DQN algorithms, that it selects random actions even after some iterations. My question is how does this approach not lead to ...
2
votes
1answer
466 views

Constant validation loss & accuracy, training accuracy fluctuates

I am training a Squeeze-net model for binary classification of images. I have 79968 images for training (50:50 for and against) and 8892 images in the validation set. After 35000 iterations my ...
1
vote
1answer
206 views

when to use dot product and when to use the common product In neural networks?

I wanted to know when to use dot product and when to not , I also don't know when we must transpose an array and why should we , could someone help me to understand this ? If you could give me the ...
2
votes
1answer
76 views

Why would a validation set wear out slower than a test set?

On this page of Google's Machine Learning Crash Course, we find the following statement: "Test sets and validation sets "wear out" with repeated use. That is, the more you use the same data to make ...
2
votes
0answers
24 views

Force Neural Net to attempt to predict every class

I am training a (deep) neural net to classify approximately 60 different classes. The range of occurrences of each class in the dataset is wide, 3 orders of magnitude from the most represented to the ...
0
votes
1answer
80 views

Train and predict on different data structures

Is it possible to train a Machine Learning model with a set of the same data structure, but use another data structure as input in prediction/classification ? If so, how ? Let me elaborate and ...
1
vote
1answer
135 views

Train neural network for regression with negative samples

I have training samples which have have vector $\vec x$ as input and a vector $\vec y$ as output - both vectors have real (float) numbers $\in \mathbb R$ as entries. I want to train a neural network ...
3
votes
1answer
1k views

Why is predicted rainfall by LSTM coming negative for some data points?

I have used supervised learning with LSTM network using tanh activation function and 0.1 dropout for time series prediction.my loss='mean_squared_error', optimizer='adam'. The predicted time series is ...
2
votes
1answer
1k views

May the training set and validation set overlap?

May the training set and validation set overlap? Similarly, may the testing set and validation set overlap?
1
vote
0answers
30 views

Training Gaussian Restricted Boltzmann Machines with Noisy Rectified (nrelu or ssu) linear hidden units

I'm not sure how to implement this architecture. I'm following this thesis (pages 17-19) or this paper but I'm not sure how to train it. I want to use this to extract features from raw audio. I know ...
2
votes
1answer
838 views

Gumbel Softmax vs Vanilla Softmax for GAN training

When training a GAN for text generation, i have seen many people feeding the gumbel-softmax from the generator output and feed into the discriminator. This is to bypass the problem of having to sample ...
2
votes
0answers
383 views

Matlab: setting static iterations per epoch in a CNN

I'm building a convolutional neural network using Matlab's neural network toolbox. I have code designed to cross-train the network with different data sets, using the previous network's layers in ...
1
vote
1answer
24 views

Observing a set of features while training is same as seeing it one time. How?

If we use the same training example to train multiple times, it won't bring any change to desired theta values. Can someone provide intuition behind it or some theoretical explanation to back this? ...
3
votes
2answers
442 views

Image classification with different number of training image for each class

I'm trying to train neural network for image classification with 4 different classes: Cars (22k training examples) Building (8k training examples) Pedestrian (5k training examples) Trees (1k training ...
0
votes
2answers
45 views

How do I control for some patients providing multiple samples in my training data?

I have a dataset with 50 patients. The patients are tracked over many years with a set of a few thousand features measured at somewhat random intervals. I am trying to predict a certain outcome (this ...
2
votes
1answer
168 views

Training Encoder-Decoder using Decoder Outputs

I am trying to build an encoder-decoder model for a text style transfer problem. The problem is I don't have parallel data between the two styles so I need to train the model in an unsupervised ...
5
votes
1answer
3k views

What is the difference between tensorflow saved_model.pb and frozen_inference_graph.pb?

I've re-trained a model (following this tutorial) from the google's object detection zoo (ssd_inception_v2_coco) on a WIDER Faces Dataset and it seems to work if I use ...
6
votes
1answer
24k views

Number of features of the model must match the input. Model n_features is `N` and input n_features is `X`.

I am new to data science and trying get some results. I'm applying Decision Tree Classifier. When my train and test datasets' size are not equal I get an error `...
1
vote
0answers
47 views

How can I train a model to modify a vector by rewarding the model based on the modified vectors nearest neighbors?

I am experimenting with a document retrieval system in which I have documents represented as vectors. When queries come in, they are turned to vectors by the same method as used for the documents. The ...
1
vote
1answer
65 views

How handle the add of a new feature to the dataset?

Let's say that you trained a model (eg. Random Forest) on a dataset with ten features (or columns). Now you add one or more features to the dataset. You need the information brought from these new ...
2
votes
3answers
213 views

400 positive and 13000 negative: how to split dataset up (train, test, validation)

Working on a medical diagnostic convolutional neural networking problem, and it's not obvious (to me) how the dataset should be split up. Do I have enough data to split it in 3, or should I just have ...
2
votes
1answer
299 views

Should I eliminate all ID columns and similar columns from training data? [closed]

This is a basic question so bear my ignorance. I feel like they contribute collectively in no way to the target. This is for performance and accuracy. The target is polar (0,1).
28
votes
4answers
11k views

Is it always better to use the whole dataset to train the final model?

A common technique after training, validating and testing the Machine Learning model of preference is to use the complete dataset, including the testing subset, to train a final model to deploy it on, ...
9
votes
4answers
35k views

Train, test split of unbalanced dataset classification

I have a model that does binary classification. My dataset is highly unbalanced, so I thought that I should balance it by undersampling before I train the model. So balance the dataset and then ...
4
votes
2answers
79 views

Can I use the training rows multiple times while training with different labels attached to it?

If I have a data set where for every text message, same but two labels are given. It could be that only one label has been filled. To visualise this scenario in real life, one may classify the accent ...
2
votes
3answers
8k views

Determine useful features for machine learning model

I am working with a dataset with hundreds of features. I wish to create a simple machine learning model using 7-10 features from the original dataset. My question is this: What quantitative metrics ...
1
vote
0answers
25 views

keras evaulate method results vary with equal testset

I trained segnet on a dataset of remote sensing imagery. When I run model.evaluate a set of metrics is returned. When I compile the model again with the same ...
2
votes
2answers
97 views

Mathematical algorithms to generate labels for training data

What are the possible approaches when we need to train a model, but the training dataset is really small? (Assuming we have a lot of data, just not many data are labeled) I know a library from ...
3
votes
2answers
481 views

Why can't I choose my hyper-parameter in the training set?

Say I've divided the data into 3 parts: training, validation and test. I know for example, that in Neural Networks, the number of hidden layers is a hyper parameter. Why can't I train numerous NN ...
1
vote
2answers
149 views

problems during training a MLP type of network

I trained a neural network model, a MLP type of network, where the first several layers are 1-D convolution for processing sequence type of input. However, the training process looks like as follows, ...
2
votes
1answer
66 views

How to determine the number of forward and backward passes in deep learning (CNN)? [closed]

Is there a way to determine the number of forward and backward passes in the training of a neural network using python?
2
votes
1answer
995 views

WGAN is too slow what are some ways to tweak for speed ups?

I have implemented a vanilla GAN which gave good results very fast but it had a lot of mode collapse issue, because of this I learned about WGAN which suppose to fix this, in fact they claim they have ...
4
votes
1answer
2k views

Train loss vs validation loss

I have a few basic questions about tracking losses during training. If I am using mini-batch training, should I validate after each batch update or after I have seen the entire dataset? What should ...
2
votes
1answer
96 views

Isn't computing the “tractable error” in Restricted Boltzmann Machines (RBM) intractable?

Let $v \in \{0,1\}^M$ be the visible layer, $h \in \{0,1\}^N$ be the hidden layer, where $M$ and $N$ are natural numbers. Given the biases $b \in \Re^M$, $c \in \Re^N$ and weights $W \in \Re^{M \times ...
2
votes
1answer
61 views

Basic classification question

I am wondering how I can manage a test data after using PCA or normalization and another thing like that in the classification because our model works on the representation given by its input vectors. ...
0
votes
1answer
698 views

What if my validation set is worse than my training?

I am running a CNN, on the 1st epoch my training set accuracy is 15% and validation set is 12%, by the 51st epoch my training accuracy is 87% and validation set is 13%. What is happening? What does it ...
0
votes
1answer
2k views

Error in training a merged model in Keras

I attempted to merge a VGG-16 and ResNet-50 model in Keras to benefit from the combined feature representations toward a binary classification task. I was successful in building and saving the merged ...
4
votes
1answer
828 views

“other” class in Image classification

In the MNIST dataset, you have 10 defined classes, one for each digit. But you don't have a "not a digit" class. It seems that most image classification datasets are the same. But in a business ...
1
vote
1answer
164 views

Any difference between adding epochs and duplicating data for neural nets?

Let's say I am training a neural net (e.g. convolutional network or LSTM). Generally, the longer the training (more epochs) leads to better accuracy, albeit at times at the expense of overfitting. ...
0
votes
1answer
97 views

How do machine learning models (e.g. neural networks) get better over time from new data?

I'm a complete newbie to the world of machine learning, and I'm currently working on implementing a model that will need to incorporate feedback, as well as changes to the data set (both feature & ...
0
votes
1answer
855 views

Validation accuracy for neural network

When training a neural network, I usually plot the accuracy obtained on the validation data (validation accuracy) as an intermediate measure of the network's performance – the final measure being test ...
2
votes
1answer
77 views

Including biased data in training model

My friend is in the business of getting cats to the top of mountains. He currently uses a set of heuristics to decide which cats are most likely to get to the top. For the ones he likes, he feeds, ...
1
vote
1answer
369 views

The sum of probabilities is more than 1

I have CNN architecture for object detection ( one object in image ) in KERAS. It has 22 Convolutional layers ( layer includes max pool , LeakyRelu and Batchnorm ) The last layes are following ...
1
vote
0answers
95 views

What does the training time for a Neural Network include?

I recently developed a DNN model and I want to know what exactly is training time and what all steps are included in it? For ex I carried out the following steps 1) Determined best Network ...
0
votes
1answer
39 views

Data preparation for Regression Model

Hi I'm currently trying to predict if an item will be successful in my store, this means (How much is going to sale in USD) My training dataset contains many features: Item name Item weight Item ...
0
votes
1answer
77 views

When it is a good idea to train a model also on the test data?

I have a small restricted dataset, it is not very small but accuracy will be much better if I will have more data. I have split it to train and test datasets: 85%/15%. I have chosen NN model and ...
0
votes
1answer
61 views

Training neural network classifier with one class after another

Is it possible to train a neural network classifier with only one class, and after that with only another class? For example, first train it only on recognizing dogs, and after finishing that ...
1
vote
1answer
263 views

Export dataset with predicted target - Python

I've this code (part of predictive model): ...
7
votes
2answers
2k views

How to fix class imbalance in training sample?

I was very recently asked in a job interview about solutions to fix an imbalance of classes in the training dataset. Let's focus on a binary classification case. I offered two solutions: oversampling ...

1
4 5
6
7 8