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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Multiple kernel SVM is equal to one ANN - Is Kernel SVM better that one ANN?
I'm comparing multiple Kernel SVM with one neural network, e.g one ANN with one hidden layer.
I have succesfully trained a neural network by using multiple Kernel ...
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Basic neural network to solve ordinary differential equation
There is a related question in the forum:
Solving an ODE using neural networks (via Tensorflow)
But I am trying an idea to solve ODE by considering that the solution would be a polynomial and the ...
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Learning the gradient descent stepsize with RL [closed]
Problem statement:
I've been working on a project to accelerate the convergence of gradient descent using reinforcement learning (RL). I want to learn a policy that can map the current state of ...
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Where can I get 5000+ classified images of zoo animals? [closed]
please help! We are college students doing this for a project. The project is using neural networks and want to build a model that takes in an input of a colored image of an animal and outputs the ...
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How to balance labeled datas and then carry out execution with a certain ratio?
I'm building a binary classification model using a neural network, with python and the libraries tensorflow and keras.
For that I have an unequal amount of labeled data: Around 2'000'000 labeled with <...
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How to recognize if a model is overfitting?
I'm trying to develop a real-time YOLOv8 model for detecting falls in a home environment.
The dataset I used consists of approximately 1100 images labeled as "fall" and "nofall," ...
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How to label a dataset of text pairs to use it as a universal one for calculating the precision@k metric for different models?
I am facing a semantic search problem. I am fine tuning different NLU models and i want to use precision@k as my main metric. Is it possible to label a dataset of text pairs to use it as a universal ...
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How can I use a model trained with batches to make predictions with single sample?
I'm training a PyTorch model with batches of 128 images, and after going through multiple convolutions, they're flattened (with .flatten) before being passed to a ...
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Why not using segmentation architectures for object detection?
Current object detection architectures like Faster-RCNN and YOLO seem to be overcomplicated in comparsion with segmentation architectures like Unet. So, why can't we just draw some rectangles around ...
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In MLP, multiple classes, using batches, For update weights, Would I have to calculate the accumulated error(of all samples) of each output neuron?
In Multilayer Perceptron neural networks, I know that there are two types of training: online training, and batch training, which consists of dividing the samples and updating the weights using the ...
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Empty Confusion Matrix and Zero Precision/F-score
Could you please say why I'm getting this warning while doing a binary classification using Artificial Neural Networks? The data are colored images.
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What does it mean if a neural networks starts overfitting more after applying regularisation techniques
Background
I am building a CNN to categorize cytometric cell data into healthy and diseased groups.
The architecture looks as follows:
3 Convolutional layers followed by average pooling followed by 3 ...
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How to find distance between class representation's decision boundaries for a neural network?
I have a 5 layer DNN with data containing 10 classes. To study how the model works, one thing among many I am looking at is the class wise representations.
I can extract the representations of each ...
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Whats the advantage of single target neural network over multi target neural network?
So right now I have made 2 neural networks to predict x and y coordinates separately. One for x, and one for y. I'm looking for a reason to backup my assignment. I have search for this and most of the ...
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Runtime Error: one of the variables needed for gradient computation has been modified by an inplace operation:
I have the following code for a reinforcement learning using proximal policy optimization. It gives the following run time error.
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Implementing Dropout in Keras
I think I am not conceptually understanding "Dropout" in neural networks. I was under the assumption that a keep rate of 0.8 would set 20% of all the neurons to 0 for each training example.
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Why the training accuracy stays high but validation accuracy does not change?
I have a binary classification problem. I get ROI mammogram images and then apply a decomposition algorithm and as output I get 5 images which summation of them results in the original image. Now, ...
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Best model for regression in this case?
I am doing some modeling to predict a variable of interest given a big set of features (500) for which I expect a considerable amount of interactions happening at least among some of them.
I first ...
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Is improving a Neural Network really just "trial and error"?
After asking on StackOverflow, I was redirected here, so I'm reposting this question.
I am a PhD student in Computational Physics and I've started to study a bit of Neural Networks, and decided to try ...
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Minimizing error in cosine similarity
Presume I have a vector space, and I am attempting to compress it into a latent vector space, while minimizing error in cosine similarity between entries. Suppose that I know the actual cosine ...
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Why isn't the validation data loss close to the test data loss?
First I set aside about 15% of my data as test data. Then, I used tensorflow.keras to create a relatively simple neural net model. Then I set the model.fit() parameter validation_split=0.2, so 20% of ...
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Is there a Tensorflow built-in function to create a matrix from a single-layered feedforward neural network without activation functions?
In Tensorflow, I implemented a simple single-layer feedforward neural network with N inputs and N outputs without activation functions and biases. Simply, it is just a N-by-N matrix. Question: is ...
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Why the test accuracy showing some odd behaviour in comparison to train accuracy?
I am currently training an ANN using Sequential(a class from Keras API within tensorflow), and I am optimizing the model's architecture and came across something I have not seen before.
The graph of ...
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Holding batch size constant, will a bigger dataset consume more GPU memory?
If you hold (mini) batch size constant (as well as everything else) but increase the number of examples (and therefore the number of training iterations), should you expect a (significant) increase in ...
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Neural Net not able to learn simple analytical equation
I am currently making my first attempts with Pytorch. I am trying to solve a simple equation with a neural net. Analytically solved, the result of my neural net shall look like this:
$$
y = \frac{x_5}{...
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activation=tf.keras.activations.relu vs activation='relu'
Both models are for binary classification problems
Model 1
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Confusion with tensorflow's Sequential Dense Layers
I'm working on a regression probem using Tensorflow, and have created two models with slight differences in their first Dense layer.
The Models
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Neural Network for binary classification not working
I have made a neural network that was working correctly as a multi-class classifier, but after changing the loss and the activation function, plus the output layer to just 1 neuron, it is not working ...
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Should non-trainable functions be part of a nn model?
Some explanation for the somewhat obscure title:
I want to train a model which can produce images given some input data. However, actually I want the model to learn some abstract representation which ...
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Reinforcement learning
I am working on a sentiment analysis project. I used BERT model for training but lack of data it gives huge overfitting. So after i moved LLM approach to do that. Using LLM finally i got good results....
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Determining the start and stop of vehicle movement by GPS
There is a large fleet, slightly more than a million vehicles, which is constantly growing. Each vehicle sends GPS coordinates to the server, as well as events (ignition is on, door is open, parking ...
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What is a proper activation function with simulated-annealing trainer for neural network?
I'm developing a gpu-accelerated simulated annealing based neural network trainer library. Currently its stuck on how to converge on "array sorting by neural network 3:10:20:10:3 topology".
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ML paper reproducibility
How can I reproduce results in an ML paper if I don't have the identical resources to train the models as in the paper ? (in my case I only have a laptop spec NVidia gpu and in most of the papers I ...
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LSTM Training Interpretation
I have an LSTM and have the following chart showing training validation performance by epoch:
Could someone explain? How can my validation performance be better than my training performance in ...
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Using Embedding For Regularization
Is using embeddings for regularization a valid practice?
My reasoning for that is that encoding training/tests datasets into smaller vectors would allow a smaller network with fewer parameters and ...
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How to handle different sequence sizes during training and production use?
My training data consists of sequences that are mode of tokens.
The objective of my model is to predict score for each token in the input sequence. This score depends on the particular token itself, ...
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Are "textbook backpropagation" still relevant?
The above backpropagation algorithm is taken from Shalev Shwartz and Ben-David's textbook: Understanding Machine Learning. This algorithm is described in the same way as the one in Mostafa's textbook, ...
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Convolutional networks: remove useless features?
I'm new to convolutional neural networks and have two related questions:
If all the filters would have the same weights initially, they would all detect the very same feature - so it would be useless ...
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Can we show only results of some epochs in tqdm?
I am training a NN and use tqdm for showing the results. However, the bad thing is that it shows the results for every epoch. This is too many as I want to train NN for atleast 500 epochs.
Is there ...
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I build my first neural network! What's next?
For an assignment we are given a dataset and we need to build a neural network to make new predictions.
Personal milestone; I built my first neural network!
I followed the following steps on my own ...
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How to improve accuracy on a single class out of 3 classes in model
I am training a classification model with 3 classes using a deep neural network.
The classes have been resampled and balanced.
I have around 600000 samples... equally distributed.
The dataset is also ...
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Reduce false positives having imbalanced data
I'm using a DNN-48 having the following scenario:
Features: 8 (48 at the end because I generate conditional sequences of 6 elements each)
Classes: Y=0 (90%), Y=1 (10%)
Precision and recall are good ...
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This simple python Feed forward Neural Network isn't learning. What am I doing wrong?
The backpropagation procedure is taken from the approach outlined in here.
Here is the code, commented:
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Training loss is much higher than validation loss
I am trying to train a neural network with 2 hidden layers to perform a multi class classification of 3 different classes. There is a huge imbalance to the classes, with the distribution being around ...
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Why I am getting error in dataloader in defining a NN?
I am trying to write a NN. However I am getting error.
Here is my Code:
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Appropriate input size for nn.Embedding
I’m quite new to using Pytorch and deep learning. What size of unique categories of a categorical variable is appropriate for applying the nn.Embedding ideally (best practices)? for example, if a ...
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Why do I keep on getting ResourceExhaustedError while training on video data using CONV3D on tensorflow?
I'm encountering a memory allocation problem while training a deep learning model on my computer, which has a Core i9 10th Gen CPU, 64 GB of RAM, and an NVIDIA GTX 1660 Super with 6GB of VRAM. Despite ...
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Why my validation loss and accuracy decays over epochs?
Im trying to build 2 simple networks with cleaned dataset for tweets sentiment classification(0/1):
one with all dense layers(binary bag of words)
another with RNN layer(embedding layer).
But it both ...
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Tensorflow diagram for attention mechanism
I was reading the tutorial from tensorflow on the transformer model, however, when they explain the transformer model, they display such a picture :
which I don't understand. What do the ingoing ...
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What Model to Choose for a NN with a Very Wide Output Layer?
The input of my neural network consists of 20 features, whereas the output consists of 20,000 of them (predicting a "quantum classical shadow" based on a few parameters: the rotation angle ...
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