Questions tagged [neural-network]

Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.

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How Are Kernel Weights Trained in 1-D CNN's with Multi-dimensional Input?

I have far from a perfect understanding of how 1-D convolution neural networks learn, but I think I understand how the kernel operates on 1-D input data. How does 1-D convolution work with multi-...
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Why are the values of my Y predicted the same and R-Squared Negative in SupervisedDBNRegression, Neural Networks

My model is not outputting the results I expected. I don't quite know my way around ANN. After learning how to use SupervisedDBNClassification from https://github.com/albertbup/deep-belief-network I ...
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How to calculate Efficientnet's compound scaling

I want to use compound scaling to tweek my own model, but I'm confused of how to utilize the $d=\alpha^\phi,w=\beta^\phi,r=\gamma^\phi$ in compound scaling and how to compute the specified grid search ...
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Autoencoder not learning walk forward image transformation

I have a series of 15 frames with (60 rows x 50 columns). Over the course of those 15 frames, the moon moves from the top left to the bottom right. As my input data I have a 60x50 image. As my ...
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Image autoencoder w/o thousands of dense neurons? prevent large model

I am trying to get around producing large models. If my desired output is a 120x100 image, then do I need a 120*100=12,000 neuron dense layer preceding it? ...
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Is it possible to use a Neural Network to interpolate data?

I am completely new to Artificial intelligence and Neural Networks. I am currently working on a plasma physics simulation project which requires a very high resolution data set. We currently have the ...
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Different results after each training of Keras/TensorFlow model

I have the following Keras/TensorFlow code: ...
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Regression trees for extrapolating time series data

This is a regression problem that involves predicting the price of e.g. aluminum, oil, strawberries. I have hourly and half hourly data for the weather and up to 10 different socioeconomic variables (...
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Extra feature on test set

Suppose I convert categorical data into dummy variables with get_dummies and I get these columns in the training dataset: x_A x_B x_C 0 1 0 0 0 1 1 1 0 But in ...
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What methods are there for predicting a signal?

I have a large dataset of signals (composed of time series). All time series describe the same process, but each series has a different duration (number of points). Based on these time series, I want ...
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Can anyone interpret this Recurrent Network Encoder-Decoder question?

I'm trying to earn some extra credit, so the professor won't elaborate further on what's being asked in this question: The dataset that we're given is a line-by-line file of protein sequences (...
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regressor column might have different length

I'm attempting to use a neural network to do some time series forecasting. The goal is to forecast price and I have a fewer regressors to help along like fuel prices and number of sick people among ...
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Question about a reading

So I'm trying to do multivariate time series prediction and a google search led me to this article: https://bookdown.org/singh_pratap_tejendra/intro_time_series_r/neural-networks-in-time-series-...
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Validation Accuracy Not Changing

As the title states, my validation accuracy isn't changing when I try to train my model. I've built an NVIDIA model using tensorflow.keras in python. I have absolutely no idea what's causing the issue....
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times series prediction with several regressors( using R)

Absolute beginner here. I'm trying to use a neural network to predict price of a product that's being shipped while using temperature, deaths during a pandemic, rain volume, and a column of 0 and 1's (...
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Is there a general rule for how many layers a NN should be based on the number of inputs?

I have a neural network that takes 1935 inputs, so I'm wondering if there is a general rule for how many layers the network should be. Should the number of neurons be descending by a certain amount?
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Which neural network is better?

MNIST dataset with 60 000 training samples and 10 000 test samples. Neural network #1. Accuracy on the training set: 99.53%. Accuracy on the test set: 99.31%. Neural network #2. Accuracy on the ...
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Examples of uses of neural networks where you can rigorously define desired properties of the solution?

Neural networks are often used to solve problems where we can't rigorously define what properties the desired solution should have, e.g. you can't define what a "picture of a cat" is and so ...
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Force neural network to only product positive values

I have a custom neural network that has been written from scratch in python and also a dataset where negative target/response values are impossible, however my model sometimes produces negatives ...
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Which Neural Network or Gradient Boosting framework is the simplest for Custom Loss Functions?

I need to implement a custom loss function. The function is relatively simple: $$-\sum \limits_{i=1}^m [O_{1,i} \cdot y_i-1] \ \cdot \ \operatorname{ReLu}(O_{1,i} \cdot \hat{y_i} - 1)$$ With $O$ being ...
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Retraining with the same data returns different accuracies

I am using TensorFlow to train a simple neural network (3 sequential dense layers). The problem is that the accuracy changes a lot every time I retrain it from scratch. I understand that, since the ...
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1answer
105 views

Combining heterogeneous numerical and text features

We want to solve a regression problem of the form "given two objects $x$ and $y$, predict their score (think about it as a similarity) $w(x,y)$". We have 2 types of features: For each ...
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Neural network weight initialization

I was working on recreating the Convolutional Neural Network Le-Net 5. I was getting around 96.5% accuracy on the training set. This was not near the 99.2% the network was meant to be operating at. ...
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Recurrent models for asynchronous / mixed frequency time series

What are some of the RNN/LSTM models for handling mixed frequency/asynchronous time series data, such as macroeconomics, financial, precipitation, etc.? So far I have found phased lstm from a similar ...
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How to train a neural network where computing the loss requires multiple object values?

I want to train a function that given metadata about an image produces hyper-parameters for an algorithm which operates on the image. My understanding is (please forgive me I'm a novice here) a neural ...
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“Saliency map” of perceptron?

I am using keras currently, and I want to see which inputs the model is "looking at". It would be like a saliency map, but my model is a simple two-layered perceptron for classification, so ...
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Do grouped convolutions actually improve learning?

My Understanding of Grouped Convolutions Let say we have some data with the dimensions [100,100,32] (lets ignore batch size and assume channels last) and we want to ...
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Vanishing gradient problem even after existence of ReLu function?

Let's say I have a deep neural network with 50 hidden layers and at each neuron of hidden layer the ReLu activation function is used. My question is Is it possible for vanishing gradient problem to ...
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Early stopping with class weights / sample weights

I'm performing a classification of imbalanced multiclass data using a Neural Network in the TensorFlow framework. Therefore I'm applying class weights. I would like to apply early stopping to reduce ...
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1answer
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KL-divergence to compare ML models

Let us say we have to neural network architectures, A and B and we train $x$ times each of them. Based on the $x$ retrainings, we can calculate $x$ prediction errors for each model, and plot its ...
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text classification - does number of features matters?

I'm working on a multi-class text classification project that aims to assign a "new bug" to his "final group assignee" To do that I was able to extract ~17000 samples and divided ...
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1answer
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Question regarding training data in word2vec - skip-gram

I have a very simple question regarding the training data in word2vec. In the skip-gram implementation, the training data (if I understand it correctly) is generated as pairs of words like it's shown ...
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Don't understand Channels in Covolutional Layers [duplicate]

I'm struggling to understand the concept of 'Channels'. What does a channel mean in the context of an image. I understand that a grey scale image only has 1 channel, and a RGB has 3, but then I see ...
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What is the use of the ID field in the source code?

Building a One Hot Encoding Layer with TensorFlow One-Hot Encoder Check out the following source code: ...
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How can I approach this neural net problem? [closed]

Suppose, I have the following data-set: ...
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Conditional variational autoencoder: Feeding labeled MNIST to encoder with Keras

I am looking for a code implementation of a CVAE using MNIST in Keras. I found this Youtube video: https://youtu.be/8wrLjnQ7EWQ that does VAE, but I am not sure how do I convert this and make encoder ...
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Neural Net gradient descend

I was planning on making my own neural network library in C++ and was going through other's code to make sure I am on right track. Below is a sample code that I am trying to learn from. Everything in ...
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global average pooling in PyTorch: torch.nn.AvgPool1d vs torch.mean

To implement global average pooling in a PyTorch neural network model, which one is better and why: to use torch.nn.AvgPool1d() and set the kernel_size to the input dimension or use torch.mean()?
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Training neural network to emulate a hash function

A hash function takes an input, performs a set of complex operations and then produces an output. For my purposes the output from the function will always be the same for any given input. I remember ...
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how to calculate parameters of an RNN using backpropagation

I'm trying to find out the two binary inputs are identical or not using RNN. my architecture is like this: I have the following functions: Where vT is the transpose of vector v and the activation ...
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How do convolutional layers in a CNN feed forward when there is multiple input feature maps?

I've been trying to recreate LeNet 1(LeNet 1 architecture is pictured in the top diagram) in python using NumPy. I am unsure of how the forward pass works when there is multiple Input feature maps in ...
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Problem in convergence of hebbian learning approach for Fuzzy Cognitive Map

I was trying to learn Fuzzy Cognitive Map by Active Hebbian Learning approach from here. What I have understand is that the model learns iteratively, at each step a new concept values enters and tune ...
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Character Level Embedding in Sentence Classification

I'm working on an NLP task that requires the use of character level embeddings. By using tokenizer library I realized that it tokenizes such as lower integer meant the most frequent character. Is ...
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Adding noise after LSTM layer

I am building a Natural Language Inference neural network model that learns to identify if one sentence (hypothesis) follows from another sentence (premise). So the input to my network is 2 sentences, ...
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How to deal with catastrophic forgetting?

I have my own implementation of ppo, which I've been trying to train for days on BreakoutNoFrameskip-v4 after totally failing to get a2c past a mean reward of 10 ...
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Account for imbalanced data in a Neural Network using prior distribution

I have a dataset with 4 classes, say their distribution in the training-set is $P_{prior}(C1) = 60\% $ $P_{prior}(C2) = 25\% $ $P_{prior}(C3) = 10\% $ $P_{prior}(C4) = 5\% $ After training a Neural ...
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CNN heatmaps substantially different for different input images

I have a convolution neural network for regression, where medical scans of many people are trained to predict some continuous variable (body related phenotype). I get reasonable performance (R2 ~ 0.9)....
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modeling time series data with large number of variables

I want to model time series data of 52 dependent variable using neural networks in order to forecast these series in future . I have tried some architectures of LSTM and CNN (conv1D) models but my ...
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sklearn MinMaxScaler: Inverse does not equal original

I am using MinMaxScaler on a large dataset (2201887, 3) to normalize features. Inversed values does not match originals. I tested with the target column, first (a), I applied the scaler on 10 values, ...
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Why won't my TFJS model's accuracy exceed .508 despite loss decreasing, and the fact that it worked for a different dataset (Iris dataset)?

This post is aptly titled: my stock prediction model's accuracy just won't go past 0.5088282227516174 despite loss decreasing. I have tried so many different things, such as: Increasing batch size ...

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