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.
144
questions
19
votes
4answers
12k 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 ...
17
votes
1answer
401 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 ...
9
votes
2answers
129 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?
9
votes
2answers
1k 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 ...
8
votes
3answers
443 views
What is “backprop”?
What does "backprop" mean? Is the "backprop" term basically the same as "backpropagation" or does it have a different meaning?
8
votes
2answers
516 views
What are the learning limitations of neural networks trained with backpropagation?
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 ...
asked Aug 3 '16 at 18:05
S.L. Barth - Reinstate Monica
1,0641 gold badge6 silver badges18 bronze badges
6
votes
1answer
338 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 ...
6
votes
3answers
557 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 ...
6
votes
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--...
6
votes
2answers
73 views
Why do very deep non resnet architectures perform worse compared to shallower ones for the same iteration? Shouldn't they just train slower?
My understanding of the vanishing gradient problem in deep networks is that as backprop progresses through the layers the gradients become small, and thus training progresses slower. I'm having a hard ...
5
votes
3answers
559 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 ...
5
votes
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?
5
votes
3answers
761 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 ...
5
votes
2answers
41 views
Is the gradient at a layer independent of the activations of the previous layers?
Is the gradient at a layer (of a feed-forward neural network) independent of the activations of the previous layers?
I read this in a paper titled Mean Field Residual Networks: On the Edge of Chaos (...
5
votes
2answers
107 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 ...
5
votes
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
votes
1answer
508 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 ...
5
votes
1answer
169 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 ...
5
votes
1answer
302 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 ...
5
votes
0answers
128 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 ...
4
votes
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 ...
4
votes
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 ...
4
votes
1answer
833 views
What is the purpose of the batch size in neural networks?
Why is a batch size needed to update the weights of a neural network?
According to that Youtube Video from 3B1B, the weights are updated by calculating the error between expectation and outcome of ...
4
votes
1answer
209 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 ...
4
votes
2answers
652 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 ...
4
votes
1answer
58 views
How can I use one neural network for both players in Alpha Zero (Connect 4)?
First of all, it is great to have found this community!
I am currently implementing my own Alpha Zero clone on Connect4. However, I have a mental barrier I cannot overcome.
How can I use one neural ...
4
votes
1answer
106 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 ...
4
votes
1answer
97 views
How to perform back propagation with different sized layers?
I'm developing my first neural network, using the well known MNIST database of handwritten digit. I want the NN to be able to classify a number from 0 to 9 given an image.
My neural network consists ...
4
votes
2answers
51 views
Which linear algebra book should I read to understand vectorized operations?
I am reading the Goodfellow's book about neural networks, but I am stuck in the mathematical calculus of the back-propagation algorithm. I understood the principle, and some Youtube videos explaining ...
4
votes
1answer
130 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 ...
4
votes
1answer
41 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 ...
4
votes
1answer
77 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 ...
4
votes
1answer
325 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, ...
4
votes
1answer
149 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 ...
4
votes
1answer
42 views
How can a DQN backpropagate its loss?
I'm currently trying to take the next step in deep learning. I managed so far to write my own basic feed-forward network in python without any frameworks (just numpy and pandas), so I think I ...
4
votes
1answer
193 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 ...
4
votes
0answers
13 views
How do weights changes handles during back-propagation when there are unknown labels
I have a question about how weights are updated during back-propagation for some of my samples that have unknown labels (please note, unknown, not missing). The reason they are unknown is because this ...
4
votes
1answer
85 views
Backpropagation equation for a variant on the usual Linear Neuron architecture
Recently I encountered a variant on the normal linear neural layer architecture: Instead of $Z = XW + B$, we now have $Z = (X-A)W + B$. So we have a 'pre-bias' $A$ that affects the activation of the ...
3
votes
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 ...
3
votes
3answers
168 views
What's the function that SGD takes to calculate the gradient?
I'm struggling to fully understand the stochastic gradient descent algorithm.
I know that gradient descent allows you to find the local minimum of a function. What I don't know is what exactly that ...
3
votes
2answers
61 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 ...
3
votes
1answer
93 views
How does the neural-network know how to tweak weights for a specific neuron?
I know backpropagation uses cost and gradient descent to tweak the weights to minimize the cost. But how does it know which weights to give more weight to in the first place? Is there something inside ...
3
votes
1answer
197 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 ...
3
votes
1answer
53 views
What kind of data structures are needed to efficiently do back-propagation in a feedforward neural network?
In a feed-forward neural network, in order to efficiently do backpropagation, what kind of data structure is needed?
I know the weights can just be stored in an array, and you need pointers of some ...
3
votes
1answer
29 views
Structure discrepancy of an LSTM?
I've found multiple depictions of how an LSTM cell operates. See 2 below:
and
Each of these images suggest the hidden state is utilised differently. On the top diagram, it is shown that the hidden ...
3
votes
1answer
74 views
How is REINFORCE used instead of Backpropagation?
In neural networks with stochastic layers I've seen the use of the REINFORCE estimator for estimating the gradient (because it can't be computed directly).
Some such examples are Show, Attend and ...
3
votes
1answer
47 views
What weights should I use while back-propagating?
I've started to learn about neural networks recently and I can't find the answer to this question.
Let's assume there's a neural network (fig. 1)
So if the loss function is:
and the derivative is:
...
3
votes
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 ...
3
votes
0answers
571 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 ...
2
votes
2answers
71 views
Why is it called back-propagation?
While looking at the mathematics of the back-propagation algorithm for a multi-layer perceptron, I noticed that in order to find the partial derivative of the cost function with respect to a weight (...
