Questions tagged [backpropagation]

For questions related to the technique of backpropagation, whereby the loss, error, or correction signal calculated at the output of an artificial network output is fed back to the parameters in each layer of the network until the network's behavior converges to a training state within the required accuracy and reliability.

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29 views

Has anyone investigated iteration awareness beyond RNN and LSTM?

This question considers the convergence of an artificial networks (MLPs, RNNs, LSTM nets, CNNs) over time or over the course of epochs made up of iterations through training examples. In this ...
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130 views

Backpropagation of convolutional neural network - confusion [closed]

I've already seen many articles about this topic and Backpropagation In Convolutional Neural Networks by Jefkine (5 September 2016) seems to be the best. Although, as author said, For the purposes ...
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Why not teach to a NN not only what is true, but also what is not true?

I'm not a person who studies neural networks, or does anything that is related with that area, but I have seen a couple of seminars, videos (such as 3Blue1Brown's Series), and what I am always told is ...
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291 views

Backward Pass for LSTMs

TL;DR I am currently trying to understand the mathematics in Ger's paper Long Short-Term Memory in Recurrent Neural Networks. I have found the document clear and readable so far. On pg. 21 of the ...
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1answer
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Do we know what the units of neural networks will do before we train them?

I apologize if this is a repeated question or if this is too simple. I was learning about back-propagation and looking at the algorithm there is no particular 'partiality' given to any unit. What I ...
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1answer
1k views

How do I implement softmax forward propagation and backpropagation to replace sigmoid in a neural network?

I'm currently using 3Blue1Brown's tutorial series on neural networks and lack extensive calculus knowledge/experience. I'm using the following equations to calculate the gradients for weights and ...
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1answer
43 views

How to change a weight/bias with gradient

After watching 3Blue1Brown's tutorial series, and an array of others, I'm attempting to make my own neural network from scratch. So far, I'm able to calculate the gradient for each of the weights and ...
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3answers
2k views

What is the best XOR neural network configuration out there in terms of low error?

I'm trying to understand what would be the best neural network for implementing a XOR gate. I'm considering a neural network to be good if it can produce all the expected outcomes with the lowest ...
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485 views

How to calculate gradient of filter in convolution network

I have similar architecture like in image:CNN. I don't understand how to calculate gradient of filter F. I found these equations(source): Gradient and delta, where first equation calculate gradient ...
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3answers
515 views

CNN backpropagation with stride>1

I read that to compute the derivative of the error with respect to the input of a convolution layer is the same to make of a convolution between deltas of the next layer and the weight matrix rotated ...
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1answer
101 views

How to determine the size of biases? [closed]

I'm new to the world of machine learning. My question is how can I determine the size of the biases in a neural network (with backpropagation algorithm)? Currently, I have a 2-layer neural network (1 ...
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2answers
8k views

What is the time complexity for training a neural network using back-propagation?

Suppose that a NN contains $n$ hidden layers, $m$ training examples, $x$ features, and $n_i$ nodes in each layer. What is the time complexity to train this NN using back-propagation? I have a basic ...
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283 views

How does backpropagation work on a custom loss function whose components have magnitudes of different orders?

I want to use a custom loss function which is a weighted combination of l1 and DSSIM losses. The DSSIM loss is limited between 0 and 0.5 where as the l1 loss can be orders of magnitude greater and is ...
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1answer
227 views

Hand computing feed forward and back propagation of neural network

I used to treat back propagation as a black box but lately I want to understand more about it. I have used mattmuzr's and DuttA's explanaiton as a guide to hand compute a simple neural network. I have ...
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1answer
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A few doubts on back propagation

I'm having trouble wrapping my head around some details of neural nets and back prop. For example's sake, consider the following net, where I have separated the 'neurons' into linear nodes plus ...
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1answer
163 views

Does training happen during NEAT?

When one uses NEAT to evolve the best fitting network for a task, does training take place in each epoch as well? If I understand correctly, training is the adjustment of the weights of the neural ...
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2answers
5k views

How is gradient calculated for middle layer weights?

I am trying to understand backpropagation. I used a simple neural network with one input x, one hidden layer h and one output layer y, with weight w1 connecting x to h, and w2 connecting h to y. x--...
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1answer
299 views

How to deal with back-propagation when dealing with invalid moves in Reinforced Learning?

As discussed in this thread, you can handle invalid moves in Reinforced Learning by re-setting the probabilities of all illegal moves to zero and renormalising the output vector. In back-propagation, ...
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2answers
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Can anyone show me the derivative of Leaky RELU in C#?

I am in the process of getting back into AI programming after some time out and have been building my neural net in C#.NET. I managed to get all of the feed-forward stuff working very eloquently but ...
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106 views

Data prepared to linear regression. Can I use it with backpropagation?

I'm studying a Master's Degree in Artificial Intelligence and I need to learn how to use the Java Neural Network Simulator, JavaNNS, program. In one practice I have to build a neural network to use ...
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1answer
94 views

Is there any research on neural networks with multiple outputs for hierarchical label classification?

I had this idea of training for example a CNN on images, and having output branches at several of its intermediate layers. The early layers' output branch might then predict high-level class of ...
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1answer
100 views

What are some concrete steps to deal with the vanishing gradient problem?

I am training an ANN for classification between 3 classes. The ANN has an input layer, one hidden layer and a 3 node output layer. The problem I am facing is that the output being produced by the 3 ...
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2answers
123 views

what is the proof behind the gradient of a curve being equal/proportional to the distance between the two co-ordinates in the x-axis [closed]

In the delta rule the equation to adjust the weight with respect to error is :- where is the Learning Rate and E is the ...
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1answer
432 views

What is the relation between back-propagation and reinforcement learning?

What is the relation between back-propagation and reinforcement learning?
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2answers
174 views

Why does ReLU (and other non linearities) work?

Can someone please point me to where I can read up on why non linearities that can produce values larger than 1 or smaller than 0 work. My understanding is that neurons can only produce values between ...
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2answers
3k views

How to combine backpropagation in neural nets and reinforcement learning?

As I am trying to make an AI with reinforcement learning, I have found out and implemented a lot of things such as both these topics (NNs and RL) separately. But when trying to combine them, I have ...
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0answers
85 views

When do you back-propagate errors through a Neural Network when using TD Lambda

I have a Neural Network that I'm want to use to self-play Connect Four. The neural network receives the board state and is to provide an estimate of the states desirability. I would then, for each ...
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122 views

How to design 4D Deep Recurrent Neural Networks using Tensorflow?

I want to design a simple model that predicts the movement of coordinates with RNNs. In a typical three-dimensional LSTM model, one feature is encoded as one hot encoding, and the ...
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0answers
623 views

Forecasting and predict using matlab Artificial Neural Network [closed]

I selected the below data set for forecast and predict using artificial neural network as my final year project. https://archive.ics.uci.edu/ml/datasets/Bank+Marketing. I normalized the data set and ...
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1answer
196 views

Finding an optimum back propagation algorithm

I recently started working on very simple machine learning codes in Python and I came across a big problem: teaching the system to improve on its guesses. So this is what the code is about: I will ...
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0answers
45 views

Recommendations on which architecture to use to guess appointment

I'm currently developping an application which allows psychologists to manage their schedule and budget. As a proof of concept, I would like to create an intelligent appointment service. There can be ...
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1answer
474 views

What makes learned feature detectors specialize in CNN?

It has been shown that it is possible to use unsupervised learning techniques to produce good feature detectors in CNNs. I can't understand what drives specialization of those feature detectors. In ...
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1answer
167 views

What would an implementation of this Neural Network look like?

I'm relatively new to neural networks and was wondering what an implementation of this paper would look like. More specifically, how are the correct values of Kp, Ki, and Kd determined at run time so ...
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3answers
472 views

How do I know if my backpropagation is implemented correctly?

I'm working on implementation of the backpropagation algorithm for a simple neural network which predicts a probability of survival (1 or 0) and I can't get it above 80% no matter how much I try to ...
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2answers
578 views

Is the mean-squared error always convex in the context of neural networks?

Multiple resources I referred to mention that MSE is great because it's convex. But I don't get how, especially in the context of neural networks. Let's say we have the following: $X$: training ...
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1answer
145 views

Can a neural network learn to avoid wrong decisions using backpropagation?

I studied the articles on Neural Networks and Deep Learning from Michael Nielsen and developed a simple neural network based on his examples. I understand how backpropagation works and I already ...
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1answer
298 views

What is the order of execution of steps in back propagation algorithm in a neural network?

I am a machine learning newbie. I am trying to understand backpropagation algorithm. I have a training dataset of 60 instances/records. So what is the correct order of the process: Forward pass of ...
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2answers
608 views

Are Dreams a Form of Backpropagation?

Humans often dream of random events that occurred during the day. Could the reason for this be that our brains are backpropagating errors while we sleep, and we see the result of these ...
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3answers
2k views

How to test if my implementation of back propagation neural Network is correct

I am working on an implementation of the back propagation algorithm. What I have implemented so far seems working but I can't be sure that the algorithm is well implemented, here is what I have ...
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1answer
234 views

Why doesnt my Neural Network work?

I Build this NN in c++. I reviewed it since 3 days. I checked every line 100 times, but I cant find my error. If someone can please help me find the Bugs: 1. The output is garbage 2. The weights go ...
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1answer
79 views

Backpropagation in Decoupled Neural Interfaces

I am attempting to create a fully decoupled feed-forward neural network by using decoupled neural interfaces as explained in the paper (https://arxiv.org/abs/1608.05343). As in the paper, the DNI is ...
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1answer
282 views

What kind of algorithm is the Levenberg–Marquardt algorithm?

Is a Levenberg–Marquardt algorithm a type of back-propagation algorithm or is it a different category of algorithm? Wikipedia says that it is a curve fitting algorithm. How is a curve fitting ...
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1answer
341 views

Differences between backpropagation techniques

Just for fun, I am trying to develop a neural network. Now, for backpropagation I saw two techniques. The first one is used here and in many other places too. What it does is: It computes the ...
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2answers
120 views

How do evolutionary algorithms have advantages over the conventional backpropagation methods?

How does employing evolutionary algorithms to design and train artificial neural networks have advantages over using the conventional backpropagation algorithms?
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1answer
2k views

How to avoid falling into the “local minima” trap?

How do I avoid my gradient descent algorithm into falling into the "local minima" trap while backpropogating on my neural network? Are there any methods which help me avoid it?
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2answers
471 views

What are the limits to what can be learnt using a backpropagation neural network?

In 1969, Seymour Papert and Marvin Minsky showed that Perceptrons could not learn the XOR function. This was solved by the backpropagation network with at least one hidden layer. This type of ...
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3answers
406 views

What is “backprop”?

What does "backprop" mean? I've Googled it, but it's showing backpropagation. Is the "backprop" term basically the same as "backpropagation" or does it have a different meaning?