Questions tagged [neural-networks]

For questions about a artificial networks, such as MLPs, CNNs, RNNs, LSTM, and GRU networks, their variants or any other AI system components that qualify as a neural networks in that they are, in part, inspired by biological neural networks.

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deep learning with kfold cross validation with epochs

I am new into neural networks, I want to use K-fold cross-validation to train my neural network. I want to use 5 folds 50 epochs and a batch size of 64 I found a function in scikit for k-fold cross ...
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How to pad the output sequence during training for an encoder decoder model

I've got an encoder-decoder model for character level English language spelling correction, it is pretty basic stuff with a two LSTM encoder and another LSTM decoder. However, up until now, I have ...
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Are there any rules of thumb for having some idea of what capacity a NN model needs to have for a given problem?

To give an example. Let's just consider the MNIST dataset of handwritten digits. Here are some things which might have an impact on the optimum model capacity: There are 10 output classes The inputs ...
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1answer
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Are connections genes in a genome ever deleted or just disabled?

When a new node is added, the previous connection is disabled and not removed. Is there any situation in which a connection gene is removed? For example, in the above diagram connection gene with ...
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Semi-supervised: Can I predict the label of purposely unlabelled observations?

Let's say I have a data set with of length N. A small proportion N2 is labeled. Can I remove some labels and then 'reverse' this action with a trained neural network? I could then use the same process ...
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Should I restart doing research in artificial intelligence, after 20 years of being away from this field?

I am seeking some advice. I worked on Artificial Intelligence, Machine Learning, Neural Networks back in the 1990s, published papers, built prototypes all in academia. When I joined the workforce ...
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Is there any way to draw a neural network's connections in a nice way?

I've been working with neural networks and artificial intelligence for a while. What I'm trying to do right now is, from a genotype I have (a sum of sensors, neurons and actuators) draw how the neural ...
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What’s the difference between LSTM and RNN?

What's the difference between LSTM and RNN? I know that RNN is a network layer used in neural networks, but what exactly is an LSTM? Is it also a network layer with the same characteristics?
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Is there a way of converting a neural network to another one that represents the same function?

I have read the paper Neural Turing Machines and the paper On the Computational Power of Neural Nets about the computational power of neural networks. However, it isn't still clear to me one thing. ...
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How does the network know which objects to track in the paper “Label-Free Supervision of Neural Networks with Physics and Domain Knowledge”?

I was reading the paper Label-Free Supervision of Neural Networks with Physics and Domain Knowledge, published at AAAI 2017, which won the best paper award. I understand the math and it makes sense. ...
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How is the receptive field of a CNN affected by transposed convolution?

When computing receptive field recursively through a CNN, does a transposed convolution affect the receptive field the same way that a convolution does if the kernel and stride is the same?
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Using tensor networks as machine learning models

Tensor networks (check this paper for a review) are a numerical method originally introduced in condensed matter physics to model complex quantum systems. Roughly speaking, such systems are described ...
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What is the difference between an generalised estimating equation and a recurrent neural network?

What is the difference between a generalised estimating equation (GEE) model and a recurrent neural network (RNN) model, in terms of what these two models are doing? Apart from the differences in the ...
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Understanding the loss function in deep Q-learning

I am trying to understand how deep Q learning (DQN) works. To my current understanding, each $Q(s, a)$ functions is estimated to be a function of a feature vector of its state $\phi$(s) and the weight ...
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Difference between training accuracy and calculating accuracy with class prediction

I have trained my neural network with a dataset of 11200 images, and its validation accuracy was 96%. I saved my model and load its weights to the same neural network. I chose 738 images of my dataset ...
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Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling?

Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling? If not, why do they perform as well as networks which use max-pooling?
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Can neural network be trained to solve this problem?

I'm working on a problem that given a dataset; where each train example is a binary matrix $X_i$ with dimension $(N_i,D_i)$ (think a training example is a feature matrix) each entry is either 1 or 0. ...
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Can a second network take as input the weights of a first network and help training the first network?

I understand that as a network learns about an output with regards to an input, weights are updated according to how wrong the guess was for that node. So, over time, the weights move in the "...
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How can I use Keras on a mobile app? [closed]

I have a trained network for machine translation. I want to use Keras on mobile and only load the weights to the app. I found keras.js, but I don't know how to use it for a mobile app. I use ionic4 ...
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In deep learning, do we learn a continuous distribution based on the training dataset?

At least at some level, maybe not end-to-end always, but deep learning always learns a function, essentially a mapping from a domain to a range. The domain and range, at least in most cases, would be ...
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1answer
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Reinforcement-learning: grey-scaling vs color of CNN input. Tradeoff?

I'm doing reinforcement learning and have a visual observation as state input for my agent. In the Deepmind Atari paper they greyscale the input image before they input it into the CNN to reduce the ...
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1answer
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Which ANN can solve for y = x * x + b?

I am new to ANN. I am trying out several 'simple' algorithms to see what ANN can (or cannot) be used for and how. I played around with Conv2d once and had it recognize images succesfully. Now I am ...
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1answer
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How can I train a neural network to detect subliminal messages?

Is there a way to train a neural network to detect subliminal messages? Where can I find the dataset on which to train the neural network? If I have to create the dataset, how would I go about it? ...
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What is the expected value of an IOU in this case?

I have a detection problem. An object with a probability of 0.5 is in a box with coordinates ((0,0), (2, 2)) and with a probability of 0.5 a box with coordinates ((2,0), (4,2)). What is the maximum ...
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What sort of Neural Network is best suited to predicting a future purchase?

I have previously implemented a Neural Network with Back-Propagation that was able to learn Tic-tac-toe and could go pretty well at Connect-4. Now I'm trying to do a NN that can make a prediction. ...
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How to reduce fluctuation of a neural network?

I've modeled an AlexNet neural network, with 50 epochs and a batch size of 64. I used a stochastic gradient descent optimizer with a learning rate of 0.01. I attached the train and validation loss and ...
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What is the difference between TensorFlow's callbacks and early stopping?

What is the difference between TensorFlow's callbacks and early stopping?
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1answer
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In neural networks, what does the term depth generally mean?

Is it number of units in a layer number of layers overall complexity of the network (both 1 and 2)
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1answer
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Is traditional machine learning obsolete since neural nets, deep neural nets can always outperform them?

I have been coming across visualizations showing that the neural nets tend to perform better as compared to the traditional machine learning algorithms (Linear regression, Log regression, etc.) ...
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Conv-2 CNN architecture - CIFAR-10

I have a CNN architecture for CIFAR-10 dataset which is as follows: Convolutions: 64, 64, pool Fully Connected Layers: 256, 256, 10 Batch size: 60 Optimizer: ...
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1answer
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Cross entropy loss function causes division by zero error

I am building a NN for which I am using sigmoid function as the activation function for the single output neuron at the end. Since sigmoid function is known to take any number and return value between ...
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How does the BERT model (in Tensorflow or Paddle-paddle frameworks) relate to nodes of the underlying neural-net that's being trained?

The BERT model in frameworks like TensorFlow/Paddle-paddle shows various kinds of computation nodes (like subtract, accumulate, add, mult etc) in a graph like form in 12 layers. But this graph doesn'...
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How to solve the problem of variable-sized AST as input for a (convolutional) neural network model?

In my work I have a given source code for a module. From this module I generate an AST, whose size is dependent on the size of the module (e.g. more source code -> bigger AST). I want to train a ...
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2answers
428 views

How to decrease accuracy from 99% to 80%~85% using keras for training a model

How do I decrease the accuracy value when training a model using Keras; which parameters can I change to decrease the value? My objective is not to actually decrease it, but just to know which ...
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Finding effect of inputs on output (shapely values)

I've developed a neural network which takes in n inputs returning m outputs. I want to see which inputs contribute most with each output. One idea I had is for all inputs/output combinations, lock ...
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1answer
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What is “temporal depth”?

I need some explanation about the following paragraph (page 3) from the paper A Novel Approach for Robust Multi Human Action Detection and Recognition based on 3-Dimentional Convolutional Neural ...
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What will be the sequence of steps in a human activity recognition model using LSTM?

In the context of these steps detection, tracking, action classification and activity recognition. Which step will be first and further sequence?
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1answer
30 views

In a neural network, can colors be used for neurons in place of floating points and would there be any benefit in doing so?

Firstly, some context. I have been reading and watching videos on the subject for around 3 years, but I am still very much a beginner in machine learning and artificial intelligence. That said, I ...
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6answers
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Can an AI be trained to generate the outline of a story?

I know that one of the recent fads right now is to train a neural network to generate screenplays and new episodes of e.g. the Friends or The Simpsons, and that's fine: it's interesting and might be ...
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Is it possible to convert Neural Network code in Python into Matlab code? [closed]

I want to convert the code written in Python into Matlab code. May I know is it possible to do that. Share the available ways or methods to do the conversion. May I know is there any Online ...
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What is the use of concatenate layer in CNN?

I am not asking what does concatenate layer does in general in point of mathematical operation. But at feature level, what significance does it provide. Does it helps removing false negatives or does ...
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How to encode board before input into the neural net?

Currently I'm working on an educational project (implementation of AlphaZero approach to different types of board games). My biggest concern at the moment is how to encode board before input into the ...
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1answer
34 views

What is teacher forcing?

In the paper Neural Programmer-Interpreters, the authors use the teacher forcing technique, but what exactly is it?
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Why does C++ seem less widely used in AI?

I just want to know why do Machine Learning engineers and AI programmers use languages like python to perform AI task and not C++ even though C++ is technically a more powerful language than python.
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What models will you suggest to use in Industrial Anomaly Detection and Predictive analysis on live streamed data?

I have been working on industrial data, that is fed live, I want to explore a few models which might suit for this the best. The data are KPI data from the manufacturing Industry.
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1answer
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Semantic Segmentation For Multiple Objects When Trained On Single Object

More of a conceptual question here: I'm working on semantic segmentation tasks in the medical space using the U-Net. Let's say that I train a U-Net model on medical images with the goal of segmenting ...
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1answer
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How does batch size affect model size?

I'm suffering from a significant brain fart while trying to get my head around how does batch size affect overall model size e.g for CNNs. Does it serve as an additional dimension for all the weight ...
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1answer
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Should I prefer the model with the lowest validation loss or the highest validation accuracy to deploy?

I trained a ResNet20 on Cifar10 and obtained the following learning curves. From the figures, I see at epoch 52, my validation loss is 0.323 (the lowest), and my validation accuracy is 89.7%. On the ...
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Hand-Signs Recognition using Deep Learning Convolutional Neural Networks

I am developing a CNN model to recognize 24 hand-signs of American Sign Language. I have 2500 Images/hand-sign. The data split is: Training = 1250 Images/hand-sign Validation = 625 Images/hand-sign ...

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