Questions tagged [deep-learning]

a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hidden layers), but also with some sort of Probabilistic Graphical Models.

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nn.NLLLoss() gives negative result - what it's mean?

I saw code which use nn.NLLLoss() (negative log likelihood loss). I looked on the results and some loss results (result of ...
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Same layer for different purposes

We are using convolution for different purposes, image reconstruction for output of transposed convolution in the decoder part of U-NET, as FCN at the last layer of U-NET, and feature extractor in the ...
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Best Neural Networks for Models with If Statements

If I am trying to use a neural network to learn/improve/replace part of an existing computer model, and that has if statements in it which seemingly make it more difficult to predict than other parts ...
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does constant input add information in training a neural network?

I am working on an image denoising task. My noise pattern is generated from a 3rd degree polynomial function of images. I have multiple sets of 4 images (called tables) to generate different noise ...
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How to see Latency at layer granularity in a CNN

I am finding documents or an example that measure Latency at layer granularity in the AlexNet model. Please could share or tutorial for me. Many Thanks
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Is batch size of 1 a valid choice for a very deep neural network with high memory requirement?

I am training a very deep neural network (Panoptic-DeepLab) with a ResNet34 backbone on Google Colab on CityScapes dataset for Panoptic Segmentation, and noticed that, with a big crop size, the batch ...
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Features and LSTM

I have a problem while developing an LStm model. I have 4 feaures that I want to use to make a prediction. When I test my model with a single feaure I get average results but when I test with all 4 ...
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What is the purpose of convolutional layers (not deconvolutional) in decoder part of autoencoder?

I am trying to understand what is the meaning of convolutional layers means by autoencoders. In the encoder part, we are using it to extract features, yes, but in definition, the decoder part is used ...
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UpConvolution feature extraction [closed]

Convolution generates higher-level features from features that are going to be convolved yes, but what about UpConv? Does it behave exactly the opposite? Does it generate lower-level features that are ...
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why layer of dimension 1 is outputting image of size n

I am studying a model where landmarks from an image are calculated. The work comes from Convolutional Experts Constrained Local Model for 3D Facial Landmark Detection. I need to confirm why the ...
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Reinforcement learning using univalent and multivalent heterogeneous features

Problem introduction I have a game in which human players play levels (just like the famous casual game candy crush) where each level has its own properties and its own difficulty. In said game, the ...
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Newly discovered learning rule

Does anyone know how this algorithm performs the learning process for neural networks? I've stumbled over this solution. It works, but I don't know how and why. It's neuron-local and works without ...
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How does compute required scale with number of model parameters?

GPT-3 has 175 billion parameters, required ~$3.114 * 10^{23}$ FLOPS, and took approximately one month to train on ~10k Tesla V100 GPUs. It seems commonly stated that the brain has the equivalent of ~...
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On-Device Football Detection Model not performing up to the par ; misdetections

I have trained a football detection model. I have so far trained the models using RCNN, SSD (backbone MobileNet), CenterNet and others. SSD and Centernet, so far have been the best in terms of speed ...
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How to Calculate values inside DBLP collaboration un-directed network? | Using python

(EDITED- thanks) I have loaded txt file (com-dblp.ungraph.txt) which includes network to a COLAB notebook. I want to calculate describe vals of the net in the COLAB- how can I do it? Number of nodes ...
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What what will happen if all the layers of a MLP or any DL architecture are set as same in the beginning?

Setting the initial weights as all zeros will have the output dependent on the bias and setting the weights of all the neurons of a layer as same, will update the gradients in same way thus removing ...
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Small difference in metrics in KERAS for the same model

I see that the MSE metric provided by the model.fit (history) is slightly different from the MSE calculated by model.evaluate? Can anyone help? ...
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Using the dnn model on data with a small amount attribute

I have made some Medical information classification model using tensorflow and keras. I make some classification model that have two input. That are the time series data[like signal data] and two of ...
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What configuration of output neurons to use for detecting bias

I am trying to make a deep learning model that detects political bias in media articles for my local community. There are two political parties here and I have a dataset of biased articles from both. ...
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Is it possible to embed a neural network layer into decision tree/random forest?

I want to do a classification task. I designed a customed layer for it. I also want to try decision tree/random forest, but as far as I know there is no way to embed my layer into a decsion tree/...
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High level-Low Level features in U-NET

Why do the first layers of U-Net or CNN generate low-level features? Why not the last layers? What is the logic behind getting low-level features at the beginning of architecture? And yes, high-level ...
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Choosing Right Optimiser and Data Scaling

The choice of optimiser and how data is scaled are both very important things in machine learning, yet they are not hyperparameters (as far as I am aware). It is also not necessarily obvious which ...
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Why input normalization leads to worse performance?

I'm building quite simple NN with Dense layers followed by ReLU activations and I noticed something unexpected. Generally, I've been confident that normalizing the input to have mean of 0 and standard ...
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How to know which layers should i freeze in deep learning architectures for transfer learning?

I am trying to make generalizations about which layers to freeze. I know that I must freeze feature extraction layers but some feature extraction layers should not be frozen (for example in ...
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Different results between hyperparameter optimisation and actual training/val values

If I want to do a hyperparameter optimisation on a dataset using e.g. hyperband or random search, I note that some of the models being randomly chosen seem to have rather good R2 scores, MSE etc. I ...
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How to get attention weights from BERT

I am trying to make transfer learning in the encoder and decoder parts of the transformer. The encoder is a whole feature extraction part and there is the feature extraction part in the decoder too(I ...
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Label network embedding as input features for multi-label classification

Leveraging correlation between labels is an essential aspect of multi-label classification. I am trying to figure out the best approach for incorporating label correlation information for my task. One ...
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Using BERT embeddings as input for transformer architecture

I will use BERT's embedding weights (as discussed here) for embedding in embedding layers of the transformer model. But my question is: don't embeddings of BERT already go through the whole encoding ...
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ValueError: cannot reshape array of size 36276416 into shape (96,227,227,1)

I am running my LeNet code with LFW, but when I run it, I am getting the following error message: Here is the code that it is getting the error ...
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Why can't I reproduce my results in keras using random seed?

I was doing a task using RNN to predict a time series movement. I want to make my results reproducible. So I strictly followed this post: https://stackoverflow.com/questions/32419510/how-to-get-...
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Chat message classification

I am trying to build a deep learning model that can classify a chat message of variable length based on the message itself as well as the previous messages (context), all of which may be generated by ...
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What is meant by averaging inhibits it in the paper 'Attention is All You Need'?

Could anyone explain to me about the sentence below? What is meant by averaging inhibits it? Multi-head attention allows the model to jointly attend to information from different representation ...
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Object Detection for Image where border is BBOX annotation

I'm wanting to train a object detection model where it contains images of different vehicles. let's say this is a sample image for which I want to use in my training set. as you can see this image ...
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Autoencoder for feature extraction with no success [closed]

I have experimented with using an Autoencoder for feature extraction (using the encoder part, discard the decoder part, to yield features). The data is structured as follows. I have a larger dataset, ...
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Why isn't my backprop matching autograd?

Am attempting to implement backpropagation for a deep learning course but my backprop gradients don't seem to be matching the gradients you get from autograd. Here's the code: Is my math incorrect ...
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How to calculate F1 and EM Match for Question answering système in other languages

I am creating a question answering system. For my English version squad was used, they have an evaluation script in their site to calculate F1 and EM Match, but how can I calculate F1 and EM Match for ...
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Has anyone been able to run sklearn_evaluation.plot.grid_search with the "kind" parameter as "line"?

I am trying to optimize the hyperparameters (one of which is hidden_layer_sizes) of the ML learner: MLPRegressor, appealing to a visual representation to guide me ...
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Fakebert implementaion

I am trying to implement this architecture of fake bert for fake news detection, but I don't know how to feed the word embedding from Bert. Help, please.
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Time series model hardly fitting well

I'm trying to forecast Google's stock prices. I've made two models one with LSTM and another one that's Bidirectional LSTM, but the forecasted values don't converge quite well with the test values. I'...
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GAN - discriminator loss remains at a constant value while the generator loss decreases?

I am building my first GAN network, and I noticed that sometimes the discriminator loss remains at a constant value while the generator loss decreases. I couldn't find an explanation - if the ...
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3 votes
1 answer
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Performing a text classification based on a dictionary

I have been given a kind of dictionary which maps a category with a set of certain strings. A sample of the dictionary is given below: This is all I have, there is no other data. There are around 46 ...
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How to organize data to use a recurrent neural network LSTM?

I am doing an internship in bailiff society. I have to create an IA model which can improve actions to perform, based on existing timeline of actions. I've already tryed some solutions (which did not ...
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BertTokenizer Loading Problem

I loaded this BertTokenizer previously, but now it is showing, I have to make sure I don't have a local directory. In my kaggle kernel, I don't have this local directory. How to solve it? ...
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Transfer learning on the same samples for progressively more specific classification

I have a classification task in which classes exist within a directed graph. That is a class may have subclasses which share an is-a relationship with their parent class. Now, I have a relatively ...
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2 votes
1 answer
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All classification models except neural network giving 100% accuracy

I have a dataset of size (140,000, 10) containing 1 dependent variable. I used MinMax scaler on independent variables. For the target value, there is a class imbalance of 94% 0's and 6% 1's. Used ...
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How do researchers actually code novel architectures and layers?

Disclaimer: I am almost a complete novice when it comes to tensorflow, keras, coding in general, and neural networks/data science. While reading papers on novel architectures for neural nets, I see ...
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Having weird accuracy graph on deep learning binary classification model

I am doing deep learning binary classification on some data and got very weird results with the accuracy metric. In the first few epochs, it doesn't change at all but then it goes on this weird linear ...
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Biometrics identification with embeddings comparison and "unknown"/"other" class/label

This is a general or more conceptual questions about biometric classification models, based on deep learning neural networks. The goal of the system is to take a set of features (e.g. voice recording, ...
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Learning Rate Finder doesn't work with Tversky Loss, any idea?

I'm working with a UNet on a binary segmentation problem. As my dataset is extremely imbalanced (sometimes the objects I'm trying to segment are really, really small) I'd like to use the Tversky Loss ...
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How YOLO detects the object when the object is in multiple grid cells?

I have been reading various articles and watching videos on YouTube, but i cant seem to understand how does YOLO makes a bounding box for an object if it is in multiple grid cells? for example in the ...
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