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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Matrix multiplication doesn't work - no output

I am having problem in the Matrix multiplication of my Python neural network. Being still a High schooler, I know next to nothing about MM except a couple of tutorials. My Neural network was working ...
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hyperparameter tuning with validation set

For what I know, and correct me if I am wrong, the use of cross-validation for hyperparameter tuning is not advisable when I have a huge dataset. So, in this case it is better to split the data in ...
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How does Neural Network denoise an image?

I understand the mathematical formalism behind how neural networks work as a classifier or perform regression analysis. But I face difficulty to realize how they are such a great denoising instrument. ...
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How to get same accuracy with identical models in Keras and Tensorflow?

As we all know Keras backend uses Tensorflow and so it should give out same kind of results when we provide same parameters, hyper-parameters, weights and biases initialisation at each layer, but ...
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Beating Roulette with Neural Networks, YoloV3, and PyTorch

Background: I am in my last semester of electrical engineering, and I am working on my senior design project. The senior design project is a two-semester design project in which students outline, or ...
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How to generate several elements on image with input parameters

For example, i need to generate circles. I have dataset of images with non-intersecting circles and can generate random circles with DCGAN, but each circle has a different diameter. So I need to ...
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Understanding the softmax output in Youtube's recommender

This question has been asked before, but never (that I can see) satisfactorily answered. I'm reading Youtube's paper on their recommender system. The system has two elements, the first of which is a ...
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Sentiment Analysis: using a dataset (IMDB reviews) to train a neural-net and using it to predict entirely different datasets (Political articles)

We need to analyse a lot of articles relevant to political instability in a given country (things like the possibility of a coalition / a snap election etc). The problem is that I could not find any ...
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How can I get probabilities of next word with ELMO?

ELMO is a language model, build to to compute the probability of a word, given some prior history of words seen. How can I get this probability from pretained ELMO model?
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Nan in target variables Neural Network

Is it possible to train on a dataset with some nan in the target variables? I imagine a sort of loss calculation only for the given target data. Is this Doable in Tensorflow/Keras =?
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56 views

What's the proper way to do back propagation in Deep Fully Connected Neural Network for binary classification

I tried to implement a Deep fully connected neural network for binary classification using python and numpy and used Gradient Descent as optimization algorithm. ...
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Creating a map with camera using Lidar data

I want to create a neural network that learns from lidar data to create a map from image by using camera. I want to start but I need some advices to proceed. I will collect data from lidar and I want ...
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Please help me find the mistake

I'm trying to use Tensorlow on İris dataset using sublime as the text editor. I got this error "TypeError: float() argument must be a string or a number, not 'generator'"when running the session, but ...
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Which Technique should we use for predicting an integer output?

I'm working on a problem where my target feature of type integer. i.e (n_clicks). In general, if we want to predict categorical target feature then we use classification algorithms and on the other ...
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Neural network reaching local optima

I was recently trying to train a convolutional neural network to classify people as Hispanic or white (for learning purposes). I couldn't find a good dataset of just those two races, so I had to ...
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How do I implement the following type of convolution (see picture)? [on hold]

I want to use the following type of convolution in my neural network. Is this a 1-D convolution? How do I implent it in keras / tensorlfow?
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Java or Python for training and implementing Predictive ANN models in production? [closed]

What are your opinions? Should I use python since I'm comfortable with it and it is the superior language for machine learning, or should I use Java since it's what my company uses for all our ...
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36 views

Policy gradient vs cost function

I was working with continuous system RL and obviously stumbled across this Policy Gradient. I want to know is this something like cost function for RL? It kinda gives that impression considering we ...
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Is Data Mining easier then Theoretical Machine Learning ad Deep Learning [closed]

Is data mining (text mining for example...) easier than theoretical machine learning and deep learning?
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How to train the predicting boxes in a YOLO network?

I have just finished this tutorial that explains how YOLO networks work. Instead of training the network's weights with a training set, the author loads pre-trained weights and uses them to test the ...
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What would be a good threshold number to convert some categories to Not Specified

The current topic is derived from this topic: https://community.rstudio.com/t/39722 (optional to read). I have the following dataset: myds : ...
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How are convolution weights represented?

I am trying to implement the Yolo algorithm and having hard time understanding how to read the weights. Using a hex editor, I can see the data written in hex format, but I m not sure what to do with ...
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Optimizer for Function Approximation using Fully connected Neural Network

In short, my query is: Which optimizer(s) should one choose to experiment for a fully connected neural network, if she wants perfect fitting (mae < 1e-04) on the training data? Details: In my ...
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1answer
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Water Consumption Prediction Model [closed]

I have 3-year historical data of a water reservoir including input flow, output flow, reservoir water height and also weather data for those days. Since the output flow (consumption of consumers) ...
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1answer
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My network doesnt learn how to memorize noise

im trying to implement an idea i have and it involves letting the NN memorise a noise to image mapping of the cifar100 dataset. It uses a custom layer ....but even when i replace the custom layer with ...
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why multiplication (squares) doesn't work for neural networks?

Below code creates sum of 2 random numbers and then we train for 1000 examples and then we are able to predict which works fine consider the below code for creating random data : ...
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Exploratory Data Analysis for Big Data with continuous and categorical variables (mixed data types)

I have a dataset with 28 variables (6 continuos + 22 categorical). I'm using ...
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how to draw hierarchical neural network diagrams?

Tools are available for drawing deep neural networks, but what tools are available to draw hierarchical neural networks? That is multiple neural networks arranged in a hierarchy.
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How to reduce dimensionality of 3.2B categorical features?

Background: This means a dataset of 7,000 samples and 3.2B columns, which I would have to read into distributed Spark memory somehow. Obviously I want to reduce the number of columns that gets fed ...
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Keras ANN Trained Model's Accuracy change on prediction

I have trained an ANN Binary classifier using Keras. It gives 90% accuracy. After testing when I predict same data again but pass only one class then accuracy decreases to 40%. I have figured out ...
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27 views

why the sigmoid function will be 1 and 0 if we use a fully connected layer that produce a big enough positive(res negative )output

HI I am using a fully connected network that uses sigmoid if we feed a a big enough weights the sigmoid function will finally become 1 or 0 , is there any solution to avoid this ? and will this lead ...
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what type of classifier to use for a multiclass multilabel problem where the input dimensions are binary

The input dimension are (100,104,1) in shape and each value could be either 1 or 0. This is basically a multilabel multiclass problem where output needs to be mapped to a 104 bit vector. 104 bit ...
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Actor-critic: Should actor and critic networks have the same size?

I'm using an actor-critic RL approach (DDPG to be specific) and am wondering if there is some rule of thumb that says whether or not the actor NN and critic NN should have the same size, ie, number of ...
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Where is the majority of Tensorflow memory usage?

Trying to wrap my head around where Tensorflow starts to use a lot of memory. If I have a batch_size=10 and my ...
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Multi Output Regression Using Tensorflow

I have got an xlsx excel file inculuding a column for X training datas and 2 columns for multiple Y datas. How can i do this regression using tensorflow?
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Understanding the “Wide” part of Google's wide and deep

Google's wide and deep recommender model sounds really cool, but I'm struggling to believe I'm grasping the wide section right so wanted to check my understanding. Their paper says the following: ...
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37 views

CNNs: understanding feature visualization Channel Objectives (SOLVED)

I'm trying to follow a paper on deep NN feature visualization using beautiful examples from the GoogLeNet/Inception CNN. see: https://distill.pub/2017/feature-visualization/ The authors use ...
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Speed Regulation of fan using Machine Learning

Can machine learning be used for the speed regulation of fan based on the environment, how many people are present in the room and routine of a particular individual and how? How can i achieve this?
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best pre trained tensorflow model for discriminating different faces?

I have two sets of faces which i'd like a neural network to learn and discriminate. What kind of model or network is best suited for this job? Anyone with experience?
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Can we learn f(x)=1/x using a neural network exactly?

Is there a way to train a neural network as $f(x) = {1 \over x}$ precisely?
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Regularizing Neural Network for deterministic function approximation

I'm training a neural network to learn a specific pricing function, which is entirely deterministic (i.e. same inputs always produce same outputs). The training occurs with 80 million data points from ...
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What can be the cause of a sudden explosion in the loss when training a CNN (Deeplab)

I am training the following deeplab CNN: https://github.com/tensorflow/models/tree/master/research/deeplab During training I see the following loss: The first 50k steps of the training the loss is ...
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1answer
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Sliding window approach using SVR & LightGBM

I'm working on a multivariate time series forecast using a couple of ML algorithms (Neural Networks, Support Vector Machines & Gradient boosting algorithms). I need to measure the performance of ...
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How do autoencoders reconstruct images/color?

I am abit confused by how autoencoders are able to reconstruct colors in images. According to me a CNN has feature detectors that convert the image into a sequence of feature or activation maps. These ...
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41 views

2D-Input to LSTM in Keras

I have following problem: I would like to feed LSTM with train_datagen.flow_from_directory The input is basically a spectrogram images converted from time-series into time-frequency-domain in PNG ...
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Autoencoders linear latent space

According to "Linear interpolation in latent space" in https://hackernoon.com/latent-space-visualization-deep-learning-bits-2-bd09a46920df and others, the latent space representation of an autoencoder ...
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Max/Min of neurons at the hidden layer of a neural network

We have a neural network with inputs $x_1,...,x_n$, a single hidden layer with neurons $y_1,\ldots,y_m$, and outputs $z_1,...,z_v$. There are no activation functions (i.e., the activation functions ...
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Skip-gram trained on The Hobbit: no improvement in the similarity of the word representation

I've trained a simple skipgram NNLM (window size = 5) on The Hobbit. This is the rough pseudocode: ...
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Why use deep neural networks over methods like linear regression or SVM?

This is a very broad question, but I was wondering why researchers would choose a deep neural network over linear regression or SVM? As in, what are the advantages and disadvantages of both?