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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7 views

Difference between stacked lstm vs attention mechanism in LSTM

What is the difference between stacked lstm vs attention mechanism in LSTM? It seem to me that both produce the same context vector at the end.
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List of numbers as classifier input

I am trying to use my data to predict the classes of the input. My data are as the following: ...
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Multi Image to Single Image Assocaition

I have a dataset of drone captured images (non-geotaggged) which I would like to stitch to form a single image. I wanted to know what type of a neural network can I use to associate multiple images to ...
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What is the meaning of each element in input_shape of Conv1D in Keras?

I have a time-series data for 3 classes (each class is 35 second) as I extract each 1 second for 95 feature extracting so my final data has shape (105,95) (rows for time and column for feature). I am ...
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Why isn't the neural network updated after every example in the dataset

Why the neural network is updating only after the whole batch passes?
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Training Xml file for object localization neural network

My question is how to pass in xml file to train neural network. I have been working with object localization neural network (CNN). Now I have done labelled the image and make all xml files. But the ...
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Conventions for dimensions of input and weight matrices in neural networks?

Im currently learning neural networks and I see conflicting decsriptions of the dimensions of weight and input matrices on the internet. I just wanted to know if there is some convention which more ...
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How to run Neural Net on GPU without python frameworks?

I coded a deep learning model from scratch in python(using numPy) without using any frameworks like keras or tensorflow. So far my model works fine but it runs on CPU. How should i modify my code so ...
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Resnet50 combined with Faster RCNN problem

Currently I'm working on person detection using resnet50 combined with Faster RCNN and PASCAL VOC as dataset. I trained the model with 2000 images with different number epochs and with different ...
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How does Pytorch deal with non-differentiable activation functions during backprop?

I've read many posts on how Pytorch deal with non-differentiability in the network due to non-differentiable (or almost everywhere differentiable - doesn't make it that much better) activation ...
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SyntaxError BatchNormalization [closed]

I don't understand why Jupyter rises a syntaxerror on " model.add(BatchNormalization()) Can someone help me please. Thank you .
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Lower loss always better for Probabilistic loss functions?

I am working on an neural net int Tensorflow that predicts percentages for win, draw, loss for given data of a game. The labels I provide are always {1, 0, 0}, {0, 1, 0} or {0, 0, 1}. After some ...
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Configuration of CNN model for recognition of sequential data - Architecture of the top of the CNN - Parallel Layers

I am trying to configure a network for character recognition of sequential data like license plates. Now I would like to use the architecture which is noted in Table 3 in Deep Automatic Licence Plate ...
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How to draw multiple matrices (with grid, custom color per cell) in 3D with raycast?

I would like to draw multiple matrix with ray-casting in 3D. More specific like this (source) I have seen similar figure in some paper (I forgot which one). I wonder how they can draw like this. If ...
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Relationship Between Independent and Dependent variables

I calculated the distance correlation among the independent variables and the dependent variables to verify the nonlinear relationship among the variables and the values I am getting for each ...
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What are the merges and vocab files used for in BERT-based models?

The title says it all. I see plenty online about how to initialize RoBERTa with a merges and vocab file, but what is the point of these files? What exactly are they used for?
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fluctuating values for validation set only

My model's structure is ...
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Classification of moving pixels with convolutional neural networks

I have a data set with videos of moving pixels. Each video contains 32 frames, each frame is 32x32 with two pixels in white and the rest in black. I have binary labels for 800 of these obtained by ...
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Machine Learning Methods for Spatio-Temporal Output [closed]

Is there a machine learning method specialized to output a value for a
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Why are Neural Network predictions “correct”, but offset from true value? Not using any past lagged values

I recently asked a similar question, but didn't get a response that really addressed/fixed the issue. Additionally, I've done some more work since then. I'm sorry for the long question below, I just ...
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ValueError: Input contains NaN, infinity or a value too large for dtype('float32') with lstm [closed]

i'm trying to calculate the mean square error for the results of LSTM algorithm, i know that the two arrays don't have null values , how else can i fix this error? here is my code ...
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LSTM-Model - Validation Accuracy is not changing

I am working on classification problem, My input data is labels and output expected data is labels I have made X, Y pairs by shifting the X and Y is changed to the categorical value ...
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What is the difference between register_buffer() and parameter.detach() in PyTorch?

I am writing a PositionalEmbedding() module which is an implementation based on "Attention Is All You Need" using PyTorch. According to the paper, there ...
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LSTM Neural Network gets stuck in a specific state when trying to predict new states over many time periods

I have built an LSTM neural network for category, or latent state, prediction. The data is more or less of the form: x1 = continuos number from current record x2 = continuous number from current ...
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CNN Multi-Class Model Only Predicts 1 class for all test images

I am trying to build a CNN model to predict 42 classes. I used pre-trained models for this. I used Xception. This is how I have imported my dataset: ...
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2answers
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Build neural network to calculate points for a board game

I want to be able to calculate how much points each player has for a board game by taking a picture on the board game. I do this as hobby, not for university or professional purposes. I will use it ...
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1answer
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Should the weights for CrossEntropyLoss be exactly the inverse of the propotions of training data?

I have a classifier network which chooses one of three classifications, and uses cross entropy loss as the loss function. If the proportions of training data are 100:10:5 for each classification, ...
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Evaluation of Mixture Density Networks

I have programmed an Mixture Density Network model to a market price. As input I have many numerical and categorical properties. The output of the network is a probability distribution (shape, ...
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How to fix the error in the following code in Python and Keras?

I am unable to figure out the problem with the code (see below). I am also providing error trace after the code. Goal of the code is to use a dataset (with numerical and categorical variables) to ...
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Can features that are the same in every sample contribute to learning?

For simplicity, let's say that I am monitoring 4 sensors for an ongoing metric. The first column is the sensor ID and the second column is the sensor type. ...
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Why don't we transpose $\delta^{l+1}$ in back propagation?

Using this neural network as an example: The weight matrices are then $$ W_0=[2\times4], W_1=[4\times4], W_2=[4\times2]$$ To find the error for the last layer, we use $$ \delta^{[2]} = \nabla C \odot ...
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1answer
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How do I get one overall prediction, where each data point has many pictures?

My task is not a simple image -> category. I have between 5 and 10 images of an object, and I must classify it. The problem is that the category isn't "...
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2answers
43 views

Training a CNN on a large dataset

I am currently trying to build a CNN for around 100,000 images. There are 42 classes. I have used the default batch size of 32. This is how my model looks like: ...
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1answer
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Is too much or very few training sample of a specific feature hamper the neural network model?

I am analysing a technique "Sherlock" - a semantic type of column detecting technique wherein training dataset too many samples of a specific type are limited up to 15K and too few occurring ...
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Loss function bounces back up

I'm training a very simple LSTM in PyTorch. It's a single layer, and I'm using it for multi-label classification with a BCEWithLogitsLoss. My batches are shuffled,...
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Why is my loss blowing up after adding regularization

I tried to add L2 regularization to a network class I wrote however when I train it the loss blows up even though accuracy also increases. Can someone explain where I am going wrong? (I am using the ...
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1answer
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Data scaling for large dynamic range in neural networks

The usual strategy in neural networks today is to use min-max scaling to scale the input feature vector from 0 to 1. I want to know if the same principle holds true if our inputs have a large dynamic ...
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How many layers should I replace in transfer learning CNN

I am designing a convolutional neural network that I believe requires transfer learning to function in practice. The network will be a character level CNN for text classification, more specifically, ...
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What is the time complexity of learning/testing phase neural network?

What is the time complexity of feed-forward neural networks? I didn't find a book or a reliable source that's talks about this. This link provides one version by breaking the structure of a simple ...
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What is an optimal local sparse structure of a convolutional vision network?

I was reading the InceptionNet Paper, where I found quite a few references to developing a sparse network structure, but I am not clear on what this means. An ...
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2answers
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Confusion with using different classes in neural networks (training vs testing)

I am new to deep learning and I am confused about having a neural network trained on certain classes and tested on different ones. Suppose I want to have a convolutional neural network that learns ...
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1answer
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Image classification using cnn [closed]

I did image classification using CNN and it successfully classified the images but How to save predicted images to separate folder for example i have two classes cat and dog after prediction how to ...
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1answer
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How do I deal with data that has only limited target values?

I'm currently working on a small project using the D1NAMO dataset (1). I want to predict the glucose level (that is given in the dataset) based on several features: accelerometer data, heartbeat (ECG) ...
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What is the theoretical differences of Multitask learning vs Fine tuning based transfer learning?

Suppose, I have the following scenarios. I have a bunch of fruits, i.e., apple, orange, and banana. I simply made a Multitask model, where my network first tell me which fruit it is, and then telling ...
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Wich activation function for DQL

After many research, I still can't find a neat answer about this question: When I found the loss of my state-action pair. I'm only backpropagating that loss true the network and setting all other ...
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clarification on back-propagation calculations for a fully connected neural network

I am currently taking Andrew Ng's Deep Learning Course on coursera and I couldn't get my head around how actually back-propagation in calculated. Let's say my fully connected neural network looks like ...
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Finding location to get text from in images

I have the task of pulling text from forms given an image of the form. For example, the form may have the form number at the top left or top center. Knowing this, I am supposed to be able to output ...
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1answer
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How to calculate change in Loss Function w.r.t Weights for a fully connected NN

I read this article: Optimal Brain Damage by Yann LeCun, John S. Denker and Sara A. Solla. where the authors discuss about estimating the saliency of each weight of a neural network, which they define ...
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Do I need validation data if my train and test accuracy/loss is consistent?

I am trying to understand the purpose of a 3rd split in the form of a validation dataset. I am not necessarily talking about cross-validation here. In the scenario below, it would appear that the ...
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What's the best approach to classify labeled stockchart images , based on their curve

I'm working on an exercise where I have selected a lot of different stock chart images, showing the week closing of around 1200 companies at two different periode in time. I have stock charts from ...

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