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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The best way to approach creating a machine learning model for malware detection

I am trying to write a machine learning model for malware. I have been practicing with some code from here, and I am starting to understand the basics. I am struggling however to write my own code and ...
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How does an autoencoder 'fill in the blanks' in the context of a recommender system?

My understanding is that an autoencoder takes an input, produces a lower dimensional representation of the input, which should explain the original features in the dataset, and then reconstructs the ...
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Tricky stacking models in keras

I'm trying to write a model with keras, that is built as shown below: ...
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Loss increasing and accuracy decreasing

I've implemented a shallow FC feedforward neural net with 2 input nodes, 1 hidden layer with 4 nodes (tanh activation) and 1 outputnode with sigmoid activation function and binary cross-entropy loss. ...
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Deep learning with Imbalanced classes

I am trying to model a packet data with 1 dimensional CNN but I have a very imbalanced classes in my target. I have 3 classes as class 0 has 53000 cases, class 1 has 300 cases and class 2 has 150 ...
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Learning Rule fo bias weights

Consider the network : The learning rule (weight update )used for the hidden-to-output weights is: The learning rule (weight update )used for the input-to-hidden weights is: So what about the bias ...
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How to interpret the value of categorical cross entropy?

Categorical cross-entropy loss is usually used in settings where the target in one-hot encoded. Suppose I have a problem where there are 300 possible outcomes, and thus my final fully connected layer ...
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Unable to figure out the following ValueError for a Variational Autoencoder implementation

I am trying to fit a variational autoencoder (VAE) with custom loss function using Keras and Tensorflow. It's throwing a value error. I will provide the traceback. But, for the sake of completeness, ...
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Why is the “dying ReLU” problem not present in most modern deep learning architectures?

The $ReLU(x) = max(0,x)$ function is an often used activation function in neural networks. However it has been shown that it can suffer from the dying Relu problem (see also What is the "dying ...
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Failed to convert a NumPy array to a Tensor (Unsupported object type float)

I'm using Tensorflow 2 and using model.fit for training the model. but when I try to run it, it says Failed to convert a NumPy array to a Tensor (Unsupported object type float). Where I'm making ...
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Explanation on how to interpret force_plot result by using SHAP

I have applied SHAP for explaining the outcome of my neural network. For the force_plot I have obtained the following output when trying to look at multiple ...
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Neural Net that memorizes what it sees in order?

I'm sorry for this weird question, I know ML is about generalization but I have a specific use case where I'd like to build a neural network or really just a matrix, that memorizes everything it sees ...
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Forcing distinct, independent sets of outputs in a neural network architecture

I'm building a neural network architecture, which starts with an autoencoder, which reduces the dimensionality of the data and performs something akin to a PCA - finds a lower-dimensional ...
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Multiple Instance Learning when different number of instances in each bag

I am trying to do a Multiple Instance Learning for a binary classification problem, where each bag of instances has an associated label 0/1. However, the different bags have different numbers of ...
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why would you mask out padded activations from the training loss?

I've followed taming-lstm for training a LSTM model on a NLP task in batches with various sentence lengths. One of his main points is: Trick 3: Mask out network outputs we don’t want to consider in ...
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What are Some Good ML/Deep Learning Approaches to This Type of Problem

Problem Definition: You are given a dataset of $N$ different features. Most of the features are actually calculations of their own time series of set length (i.e A 5-time step weighted moving average)...
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Difference between zero-padding and character-padding in Recurrent Neural Networks

For RNN's to work efficiently we vectorize the problem which results in an input matrix of shape (m, max_seq_len) where m is the number of examples, e.g. ...
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Is there any problem with the following Python+TF+Keras code for a custom loss function and network?

I am trying to code a custom loss function for variational autoencoder. I am not using mse for reconstruction loss since I am not learning p(x|z) ~ N(mu,I). Instead ...
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Validation loss diverging away from the training loss

I used the XLNET for a sentiment classifier in determining whether a comment is positive or negative. I was able to get good results But when I plotted the validation and training losses I saw this ...
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Feature extraction from sequence of images with Siamese Neural Network

I am trying to train a neural network to recognize certain actions in short movies. Each such movie consists of a fixed number of frames, each frame - the image is of course the same size, after ...
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I am trying to score CNN for packet data [closed]

The current error I am getting is:Shape mismatch: The shape of labels (received (32,)) should equal the shape of logits except for the last dimension (received (192, 3)).
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Use Tfrecord to feed multi-input neural network

I have a dataset on Tfrecord that includes images and tabular data, I want to feed those data into a mixed neural network data. I saw examples of how to use multi-input neural network by using numpy ...
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Tensorflow - I don't get the right shapes - `ValueError: Shapes (9, 1) and (8, 9) are incompatible`

I want to train a Sequential Neural Net (NN) with Tensorflow. ...
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CNN for Intrusion Detection [closed]

I hope you are alright. I am new to Deep Learning and I am assigned a task to find out how to use CNN for Intrusion Detection. After reading about I find out that CNN is used mostly for computer ...
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How to create a multi label classification network in Keras if I have the training data with various accuracy?

I'm trying to create a neural network that finds the most effective treatment for each patient. I have a medical database for training. The inputs are histological and pathological data (mostly 0/1 ...
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Use Machine Learning/Neural Network + Distance Measurements to Find the Position of Devices (Localization)

I want to find the position of several devices using at least distance measurements. These measurements are done using a radio, and it might be that not all devices are in radio range (no distance ...
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How can I create a neural network in Keras, which can find the best patient+treatment paires from a medical dataset? [closed]

I have a medical database for training, the inputs are histological and pathological data (mostly 0/1 data of having some conditions), the outputs are the treatments. And there is an effectiveness ...
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How to build a neural network for biostatistical data? [closed]

I need to build an experimental neural network which can predict the best treatment for patients, based on histological and pathological data. (most of them are normalized to 0-1 values) For the ...
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Sigmoid activation function for scaled continuous data

I've been working on a NLP project that attempt to output a single numeric value. The natural form of the data is integers between 0 and 27, with 27 being an absolute maximum, and values above 27 ...
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15 views

Neural network type question

This web link is to a site that talks about forecasting building electricity, like a time series regression concept. In the article they talk about the NN architecture as: the architecture of this ...
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Padding in Convolution Formula

Why is it that the formula for each element in a convolution between an image $I$ and a $k \times k$ sized kernel $K$ is $$ (I*K)_{ij}=\sum_{m=0}^{k-1}\sum_{n=0}^{k-1}I_{(i-m),(j-n)}K_{mn}=\sum_{m=0}^{...
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Understanding Node Embeddings

I have only just started to look into graph neural networks and I am a little confused on the node embedding process. Here is my understanding, please let me know if i misunderstood: Given unlabelled ...
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Understanding the convolution formula

According to several sources this formula, or the center originated version of it, is used to calculate an element of a convolution between an image $I$ and a kernel $K$ of size $k \times k$: $$ (I*K)...
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Trying to contribute to Explainable AI [closed]

Respected reader, Greetings! Background A few days ago, I was attending a workshop where I came across the term Explainable AI. The speaker described it in very brief and empathized on the need for ...
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Adam Optimiser First Step

Plotting the paths on the cost surface from different gradient descent optimisers on a toy example, I found that the Adam algorithm does not initially travel in the direction of steepest gradient (...
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Semantic segmentation in high-resolution images with high variance - cannot avoid underfitting

I am working on a dataset of 2K images for a semantic segmentation problem. I want to detect and localize small objects, with the smallest mask to be 5x5 pixels. The images include 5 different ...
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How to increase accuracy and decrease loss of my model

https://jovian.ai/casella0798/badmodel I created the model above to predict red wine quality. I have 6 classes, from 3 to 8. Dataset is unbalanced, with a lot of classes 5 and 6. My model performs ...
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How to read the predicted label of a Neural Netowork with Cross Entropy Loss? Pytorch

I am using a neural network to predict the quality of the Red Wine dataset, available on UCI machine Learning, using Pytorch, and Cross Entropy Loss as loss function. This is my code: ...
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Should I give regularly-spaced or irregular-timestamped data to a price predicting neural network?

I am building an application to predict the price of an item. Data is collected at regular 5-minute intervals while the application is running. Unfortunately, there is downtime, so there is not a full ...
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Which machine learning problem is this?

I am not able to figure out what kind of machine learning is this: Training set: consists of sentences with object labels for object phrases Example: ...
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Preparing training data for NLP machine learning task

I have the natural language sentences as follows: This is a black chair. It is next to the table. Each phrase that represents an object is annotated with an object ...
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What are good resources to learn the basics of graph theory

I just spent half a day "debugging" a script that tried to get the betweenness centrality of a weighted graph with negative weights. However the package I used for that julia's ...
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13 views

Variational autoencoders - encoder-decoder neural nets relationship to maximizing evidence

I am new to VAE and I do not know why if the encoder and decoder are learned by a neural networks then how is maximizing the ELBO (evidence lower bound) or maximizing evidence in general relevant to ...
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27 views

What is the best practice for tuning hyperparameters using validation data?

I'm building a binary classifier, using task-transfer from resnet and a total training set of 300 images. Initially I put aside 100 images as validation, and tuned the hyperparameters, each time ...
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Are non-relu activations better for small/ dense datasets?

Building on the questions below, the only conclusion I could draw from the answers was that ReLu is less computationally expensive and better at sparsity. Why is ...
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Does sigmoid facilitate modeling non-linear decision boundaries or does this come from high-dimensional data?

I'm writing up a neural network using sigmoid as the activation function. According to one lecture, sigmoid simply squashes numbers onto the (0,1) interval. To model non-linear decision boundaries, ...
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17 views

Active learning with Fisher information matrix for regression with vector output

I am trying to enhance the learning capabilities of my regression task with the help of active learning. I decided to use variance reduction using the Fisher information matrix as a query strategy as ...
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9 views

Generalized Hebbian Algoithm (GHA) stability issues

The GHA proposed by Sanger in 1989 is supposed to be very numerically stable. There are some implementations of it floating around, for example this one https://github.com/matwey/python-gha. In that ...
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Regression and Classification in one Neural network

For example consider object localization problem. Here NN will have 5 ouputs. output[0] will tell probability of object present in image, other 4 will tell bounding ...
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Regression with LSTM network: use multiple time series as input

I've spent a few days on this and am starting to think I'm missing the obvious solution as this doesn't seem like a very uncommon problem. As an example dataset: I have 100 measurements with each a ...

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