Questions tagged [backpropagation]

For questions about the back-propagation (aka "backprop", and often abbreviated as "BP") algorithm, which is used to compute the gradient of the objective function (e.g. the mean squared error) with respect to the parameters (or weights) of the neural network, when trained with gradient descent.

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2answers
121 views

Which neuron represents which part of the input?

In a neural network, each neuron represents some part of the input. For example, in the case of a MNIST digit, consider the stem of the number 9. Each neuron in the NN represents some part of this ...
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1answer
213 views

Training the generator in a GAN pair with back propagation

For the purposes of this question I am asking about training the generator, assume that training the discriminator is another topic. My understanding of generative adversarial networks is that you ...
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1answer
666 views

Feed forward neural network using numpy for IRIS dataset

I tried to build a neural network for working on IRIS dataset using only numpy after reading an article (link: https://iamtrask.github.io/2015/07/12/basic-python-network/). I tried to search the ...
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1answer
372 views

What is the derivative function used in backpropagration?

I'm learning AI, but this confuses me. The derivative function used in backpropagation is the derivative of activation function or the derivative of loss function? These terms are confusing: ...
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1answer
65 views

What is the use of the $\epsilon$ term in this back-propagation equation?

I am currently looking at different documents to understand back-propagation, mainly at this document. Now, at page 3, there is the $\epsilon$ symbol involved: While I understand the main part of the ...
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1answer
46 views

Am I able to visualize the differentiation in backprop as follows?

I'm wondering if I can visualize the backprop process as follows (please excuse me if I have written something terrible wrong). If the loss function $L$ on a neural network represents the function has ...
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1answer
27 views

Training by one batch of examples, what does it mean

Say I have a batch of examples, each examples represent a state: ...
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3answers
977 views

What is the actual learning algorithm: back-propagation or gradient descent?

What is the actual learning algorithm: back-propagation or gradient descent (or, in general, the optimization algorithm)? I am reading through chapter 8 of Parallel Distributed Processing hand book ...
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0answers
35 views

Use of backpropagation for weight updates in a combination of 2 neural networks

Every neural network updates its weights through back-propagation. How is back-propagation used for updating weights in a combination of 2 or more neural networks (e.g.:CNN-LSTM, GAN-CNN, etc.). For ...
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0answers
110 views

Update of weights in Recurrent Neural Network through back propagation

How does Recurrent Neural Network updates its weights and bias through backpropagation? Is time taken into account while updating the weights of a RNN using Backpropagation through time(BPTT)?"
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0answers
52 views

Using features extracted from a CNN as convolutional filter

I'm a bit confused about this. Assume I have a CNN network with two branches: Top Bottom The top branch outputs a feature vector of shape 1x1x1x10 (batch, h, w, c) The bottom branch outputs a ...
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2answers
1k views

Should the weights of a neural network be updated after each example or at the end of the batch? [duplicate]

Should the weights of a neural network be updated after each example or at the end of the batch? Do I need a normalization factor in the second case?
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2answers
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How do we design a neural network such that the $L_1$ norm of the outputs is less than or equal to 1?

What are some ways to design a neural network with the restriction that the $L_1$ norm of the output values must be less than or equal to 1? In particular, how would I go about performing back-...
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1answer
198 views

How do I know how changes in the weights are changing the reward in Reinforcement Learning

I already know the basics of the basic of Machine Learning. E.g.: Backpropagation, Convolution, etc. First of let me explain Reinforcement learning to make sure I grasped the concept correctly. In ...
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1answer
2k views

How do I calculate the gradient of the hinge loss function?

With reference to the research paper entitled Sentiment Embeddings with Applications to Sentiment Analysis, I am trying to implement its sentiment ranking model in Python, for which I am required to ...
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2answers
174 views

How do I change the values of a neural net [closed]

I'm trying to have a go at building a neural net, but I can't seem to figure out how to optimise the connections. I've tried to have a look online and it came up with "backpropagation". I looked ...
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1answer
3k views

How to train a CNN

When it comes to CNNs, I don't understand 2 things in the training process: How do I pass the error back when there are pooling layers between the convolutional layers? And if I know how it's done, ...
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2answers
96 views

How can I implement back-propagation for medium-sized neural networks?

I've been wanting to make my own Neural Network in Python, in order to better understand how it works. I've been following this series of videos as a sort of guide, but it seems the backpropagation ...
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1answer
99 views

Why coupling coefficients in capsule neural networks can't be learned by back-propagation?

The paper Dynamic Routing Between Capsules uses the algorithm called "Dynamic Routing Between Capsules" to determine the coupling coefficients between capsules. Why it can't be done by ...
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0answers
256 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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1answer
393 views

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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1answer
665 views

How to change the backward pass for an LSTM layer that outputs to another LSTM layer?

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 pdf (pg. ...
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1answer
1k views

Do we know what the units of neural networks will do before we train them?

I was learning about back-propagation and, looking at the algorithm, there is no particular 'partiality' given to any unit. What I mean by partiality there is that you have no particular ...
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1answer
4k 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
105 views

How should I update the weights of a neural network, given the 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 an 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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0answers
710 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
1k 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
136 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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5answers
26k 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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1answer
388 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
328 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
241 views

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
300 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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1answer
7k views

How is the gradient calculated for the middle layer's 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 $w_1$ connecting $x$ to $h$, and $w_2$ ...
4
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1answer
371 views

In the case of invalid actions, which output probability matrix should we use in back-propagation?

As discussed in this thread, you can handle invalid moves in reinforcement learning by re-setting the probabilities of all illegal moves to zero and renormalising the output vector. In back-...
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2answers
4k views

What is the derivative of the Leaky ReLU activation function?

I am implementing a feed-forward neural network with leaky ReLU activation functions and back-propagation from scratch. Now, I need to compute the partial derivatives, but I don't know what the ...
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0answers
142 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
109 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
120 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
329 views

What is the proof behind the gradient of a curve being proportional to the distance between the two co-ordinates in the x-axis?

In the [delta rule][1] the equation to adjust the weight with respect to error is $$w_{(n+1)}=w_{(n)}-\alpha \times \frac{\partial E}{\partial w}$$ *where $\alpha$ is the learning rate and $E$ is the ...
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1answer
498 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
5k views

How to combine backpropagation in neural nets and reinforcement learning?

I have followed a course on machine learning, where we learned about the gradient descent (GD) and back-propagation (BP) algorithms, which can be used to update the weights of neural networks, and ...
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0answers
109 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 state's value. I would then, for each move, ...
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0answers
163 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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1answer
238 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
47 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
567 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
175 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
828 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 ...