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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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Dataset image size and inference speed

Does training a pre-trained model on a the same dataset but with sizes scaled down (e.g., by 70%) improve inference speed? More generally, does training a CNN on smaller images improve inference speed?...
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Is a good shuffle random state for training data really good for the model?

I'm using keras to train a binary classifier neural network. To shuffle the training data I am using shuffle function from scikit-learn. I observe that for some shuffle_random_state (seed for ...
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Can Adagrad be used to optimize non-differentiable functions?

I am reading a book (TensorFlow For Dummies, Matthew Scarpino), and here it says: Adagrad methods compute subgradients instead of gradients. A subgradient is a generalization of a gradient that ...
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1answer
22 views

Multivariate Time Series Binary Classification

I have continuous (time series) data. This data is multivariate. Each feature can be represented as time series (they are all calculated on daily basis). Here is an example. ...
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25 views

Comparison between addition and multiplication function in deep neural network? [on hold]

I designed a specific Convolution Neural Network to study in the area of image processing. The network has a part that there are two tensors which have to be transformed into a tensor in order to be ...
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Model the predictive relationship between images

Hello fellow machine learners, We have numerous pairs of 64 x 64 (or other dimensionality) images (maps). In each pair, the first image demonstrates a physical parameter, e.g. wind speed, at each ...
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Different testing and training accuracy values within a NN TensorFlow structure

In order to select the optimum number of my gradient descent algorithm, I had used a for loop of 1500 iterations and each 100 iterations training and testing accuracies are printed. Here everything is ...
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Text classification problem [on hold]

Am asked to extract tenders titles in a specific work-field (expl: oil sector) from the web , am really beginner in NLP , what steps and methods do i have to use ?
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How to design a LSTM network with different number of input/output units?

I want to design a LSTM network with 25 input units(T_x=25), and 5 out units(T_y=5), but I don't know how to convert 25 y^s(y_hat), to 5 y^s? For example the following picture shows a LSTM network ...
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1answer
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Doubt regarding the number of weights in 2 layer neural network

Considering a hypothetical scenario , where we have 10 input layers, and 5 output layers. How many weights are there in the neural network? If this is implemented in pytorch, the answer will be 50. ...
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24 views

Interpreting MLP output

I just wrote an MLP in Python. After having trained it, I pass in some test data to see the result, and I get an array of decimal numbers at the output, rather than the desired binary output. For ...
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Machine learning [on hold]

Please i am stuck using machine learning for find association between data just I have one column input vector and one output target i want to match the values from vector a to b (There is not ...
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Training regression LTSM model with features having more than one value per time

Dataset of amount of #Alarms, on weekly basis, there are 37 weeks. The feature data set connected to the each week, have more than one value. ...
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1answer
24 views

Product of dot products in neural network

In a neural network, it is common to compute a dot product of the form $$\langle w, x \rangle = w_1 x_1 + w_2 x_2 + \ldots + w_n x_n$$ and use it as argument to some activation function. This is ...
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1answer
109 views
+50

Understanding output of LSTM for regression

I am working with embeddings and wanted to see how feasible it is to predict some scores attached to some sequences of words. The details of the scores are not important. ...
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2answers
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Neural Network Initialization - Every layer?

Does every layer of a Neural Network require weight initialization or just the first? Does the first layer feed into the next layer and initialize itself? My intuition is that every layer needs its ...
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Construction Adjacency matrix for GNN

A bit of a novice question, my data comes in the form of 2D hit points, my goal is to perform node/edge classification to find out whether this graph is my signal or background. My question is, when ...
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How to approach edge detection using GNN on sparse data?

I'm at the beginning of a project and I really wish to try and on the way learn Graph-Neural-Networks. The goal is to find the underlying graph structure of data points, some could be more clustered ...
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1answer
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Coding MLP: good practices?

I recently finished coding my own MLP neural network in Python. To make my code easier to read, I separated the MLP, into classes; the network class, the layers class and the neuron class, where the ...
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How to shift output of predict values into the x (input) column 0 values using Neural network in python

The inputs here are the 3. The output here (LSTM) is the probabilities that the next x1 input ought to be. Means here I have x,x1,x2 input values. 1st three inputs LSTM output1 and then next if x ...
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Time Series Forecasting RNN: Masking Values

Suppose you have missing values in a time series E.g. : t1 x1 y1 t2 ? ? t3 x3 y3 t4 ? ? t5 x5 y5 You are trying to forecast this time series using a recurrent ...
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1answer
39 views

could not broadcast input array from shape (2,3) into shape (3) while using timestamp to build neural network in python

Here I want predict value every 60 minutes. So I have data 540 with three inputs. so I wrote an code with time steps and it gave me this error. Can anyone help me to solve this problem? my code : <...
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3answers
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How to handle features which are not always available?

I have a feature in my feature vector that is not always available respectively sometimes (for some samples) it makes no sense to use it. I feed a sklearn MLPClassifier with this feature vector. Does ...
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Reentry in Computational Neural Networks

I'm interested in finding an analogue of neurobiological reentrant systems in computational neural networks. It has been found that certain pathways of the brain synapse on earlier regions in the ...
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1answer
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Splitting image dataset with few subjects but many data

I'm carrying out training/testing of a convolutional neural network for facial expression recognition with various datasets - all labelled by 7 emotion classes. For other datasets, there are a large ...
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Supervisory information through side output in convolutional neural network

I am trying to implement this paper https://ieeexplore.ieee.org/document/7828014 Here they have mentioned text local (edge) and global regions as supervisory information. Side output is generated ...
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Sci-Kit Learn Neural Network Attribute Advice

I'm working on a neural network for a set of weather data, and I'm looking for advice on what attributes should be included, and which are unnecessary. The data I'm working with includes 16,000 ...
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32 views

Tensorflow Deep learning network not utilizing GPU?

I have a Nvidia GeForce GT 755M (PC), which I heard should be at least functional for running deep learning models. But when I train my model (DCGAN) and check the task manager process info (Win 10) I ...
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Why is recall so high?

I've built a binary classification model based on Keras and I am getting about 70% accuracy, and about 72% precision and 88% recall, making up to 79% F1-Score. I've tried different data models (...
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1answer
25 views

How large of a value should a weight have in a neural network?

If you're assigning random values to the weights in a neural network before back-propagation, is there a certain maximum or minimum value for each weight ( for example, 0 < w < 1000 ) or can ...
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1answer
10 views

Recognizing circled numbers on a piece of paper

I've built a handful of CNN using tensorflow, keras, pytorch for recognizing text/number/objects in an image. What I'm trying to figure out how to do now is how to recognize numbers on a piece of ...
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1answer
25 views

How to put multiple features into RNN input vector

I am trying to code a recurrent neural network (LSTM) to create music in python and was considering using multiple features instead of just the note pitch as an input into the network. Initially I had ...
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2answers
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How can I train a model for localizing objects(classification not required) in Python

I need to make a model that creates bounding box around objects(but does not classify them) for a competition. Which libraries or pre-trained models should I use. I need values of x1,x2(x1+w),y1,y2(y1+...
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1answer
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Should I expect major performance improvements by scaling my features?

I'm trying to decide whether I should scale my features & responses for training, and I'm in a situation where I can't just try both scaling and not scaling. My features currently have an std ...
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1answer
38 views

Loss Function for Probability Regression

I'm trying to predict a probability with a neural network, but having trouble figuring out which loss function is best. Cross entropy was my first thought, but other resources always talk about it in ...
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2answers
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What does the “Loss” value given by Keras mean?

I setup my neural net to use mean square error as shown below. To my understanding (and from reading the documentation) this means that if the correct result of a row is 0.7 and the net predicts 0.8 ...
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1answer
17 views

Can I use the Softmax function with a binary classification in deep learning?

I want to create a deep learning model (CNN) for binary classification, can I used the softmax function instead of the sigmoid function in binary classification? Adding the classification layer to ...
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1answer
31 views

Is there a rule of thumb when designing neural network in deep reinforcement learning?

In deep learning, we can assess model's performance with loss function value and improve model's performance with K-fold cross-validation and so on. But how can we design and tune neural network used ...
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1answer
17 views

Transfer learning - small database

I am trying to use transfer learning in medical (ultrasound pictures). The problem is - I have very limited picture database = 400 (360+40). I am using resnet50 (I don't think this is important but ...
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1answer
25 views

Using the validation data

I'm unclear on the exact process of using the validation data. Let's say that I fit my neural network model and adjust hyperparameters using the training set and validation set. Do I then evaluate ...
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3answers
79 views

How to get accuracy, F1, precision and recall, for a keras model?

I want to compute the precision, recall and F1-score for my binary KerasClassifier model, but don't find any solution. Here's my actual code: ...
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Replicating RNN within PyTorch

I tried to create a manual RNN and followed the official PyTorch example, which tries to classify a name to a language. I should note that it does indeed work. I'm not using the final logsoftmax, ...
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80 views

Odd Loss Curves for Object Detection Task

I'm re-training a Single Shot Detector (specifically the ssdlite_mobilenet_v2_coco from the TensorFlow model zoo) to detect some new images. I have about 15k images in the training set and about 4k in ...
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1answer
22 views

Is it possible to decode which neuron represent which feature and why does it represent it?

In a neural network, Each neuron in the network represents some part of non-linear feature of the input. Ex: Like in mnist data, Consider the stem of number 9 is cut into multiple pieces and different ...
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Unexpected shape of “training curves” in NN

I'm trying to find the best configuration for my NN (in terms of batch size, learning rate etc) and noticed the following unexpected behavior. The AUC scores, computed on validation data, as ...
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14 views

Dealing with Error in Neural Network input

When you are building a neural network in which the input values are known to have error is there a way to incorporate this into the network? I.e one value of the input features may have a known small ...
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0answers
39 views

LSTM Long Term Dependencies Keras

I am familiar with the LSTM unit (memory cell, forget gate, output gate etc) however I am struggling to see how this links to the LSTM implementation in Keras. In Keras the input data structure for X ...
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22 views

How to choose best model checkpoint when training deep learning model on all the data?

When training a final model for production, it's often recommended to train on all available data (train + dev + test), as discussed here. I'm training a deep learning model. I typically save and use ...
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Extract weight matrix of Convolutional Neural Network in MATLAB

I try to train Convolutional Neural Network via MATLAB and want to know the weight matrix and bias vector in each layer. The network works well but when I type "layer(2).Weights" it returns "[ ]". ...
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Provide optional confidence level as an input to the neural network

I have a name, gender labeled dataset and I know the frequency of particular name can occurred in the dataset. I want to develop a neural network which predict gender when given the name as an input. ...