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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3answers
75 views

How does adding a small change to an neuron's weighted input affect the overall cost?

I was reading the following book: http://neuralnetworksanddeeplearning.com/chap2.html and towards the end of equation 29, there is a paragraph that explains this: However I am unsure how the ...
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1answer
122 views

How are filters weights updated for a CNN?

I've been trying to learn backpropagation for CNNs. I read several articles like this one and this one. They all say that to compute the gradients for the filters, you just do a convolution with the ...
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0answers
67 views

Difficulties to implement the layer-wise relevance propagation in MATLAB

I'm having serious issues with the implementation of the LRP algorithm for neural networks in MATLAB. The challenge is to implement the equations correctly. I'm trying to implement the deep-Taylor $\...
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3answers
633 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
46 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 ...
5
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1answer
3k 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
167 views

How should the values of the filters of a CNN change?

I wrote a convolutional neural network for the MNIST dataset with Numpy from scratch. I am currently trying to understand every part and calculation. But one thing I noticed was the "just positive" ...
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0answers
49 views

How are the weights between the input and hidden layer updated in a 3 layer neural network?

Consider a feed-forward neural network with one hidden layer. How are the weights between the input and hidden layer updated, after the weights between the hidden layer and output layer are updated?
4
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1answer
103 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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1answer
98 views

When is bias values updated in back propagation?

I am new to deep learning. I have doubts on modifying bias values during back propagation. My doubts are Does the back propagation algorithm modifies the weigh values and bias values in the same pass?...
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2answers
105 views

Are on-line backpropagation iterations perpendicular to the constraint?

Raul Rojas' Neural Networks A Systematic Introduction, section 8.1.2 relates off-line backpropagation and on-line backpropagation with Gauss-Jacobi and Gauss-Seidel methods for finding the ...
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1answer
242 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

Back propagation on Flatten Layer in CNN

I am making a NN library without any other external NN lib and is implementing the Flatten layer. I know the forward implementation of flatten layer but is the backward just reshaping it or not? If ...
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1answer
119 views

What is the difference between backpropagation and predictive coding?

Reading the high-level descriptions of backpropagation and predictive coding, they don't sound so drastically different. What is the key difference between these techniques? I am currently reading ...
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0answers
112 views

How to back-propagate illegal actions for policy gradient learning

When training a AI RL agent to play a game there'll be situations where the AI cannot perform certain actions lest they violate the game rules. That's easy to handle, and I can set illegal actions to ...
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2answers
60 views

Could error surface shape be useful to detect which local minima is better for generalization?

The following plot shows error function output based on system weights. Two equal local minima are shown in green pointers. Note that the red dots are not related to the question. Does the right one ...
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1answer
41 views

Which local minima to choose according to the shape of the error surface?

The following plot shows error function output based on system weights. Two equal local minima are shown in green pointers. Note that the red dots are not related to the question. Considering the ...
16
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2answers
10k 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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0answers
125 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
141 views

How does backpropagation with unbounded activation functions such as ReLU work?

I am in the process of writing my own basic machine learning library in Python as an exercise to gain a good conceptual understanding. I have successfully implemented backpropagation for activation ...
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1answer
164 views

A neural network for digits recognition doesn't work (MNIST, Numpy) [closed]

I'm a beginner in machine learning and I was trying to make a test neural network for digits recognition from scratch using Numpy. I used MNIST dataset for training and testing. Input layer have 28*28 ...
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1answer
323 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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2answers
257 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
2k 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
40 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
24 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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0answers
96 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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4answers
457 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
28 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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1answer
446 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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0answers
48 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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1answer
70 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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0answers
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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1answer
311 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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1answer
409 views

How to find partial derivative of softmax w.r.t logits in python

i have trouble implementing back propogation for multi class classification of CIFAR10 dataset My neural network has 2 layers forward propagation X -> L1 -> L2 weights W are initialized as random ...
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2answers
150 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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2answers
4k 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 ...
4
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1answer
75 views

Backpropagation With Medium-sized Neural Networks

So, 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 ...
5
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1answer
2k 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, ...
5
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1answer
491 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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0answers
160 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 ...
2
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1answer
106 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 ...
6
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1answer
269 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 ...
4
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1answer
1k views

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 ...
17
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1answer
381 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
2k views

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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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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0answers
526 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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2answers
134 views
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0answers
302 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 ...