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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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15
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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 ...
15
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1answer
366 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
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3answers
422 views

What is “backprop”?

What does "backprop" mean? Is the "backprop" term basically the same as "backpropagation" or does it have a different meaning?
9
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2answers
667 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
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2answers
121 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?
6
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3answers
489 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
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2answers
476 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 ...
6
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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--...
6
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2answers
59 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
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1answer
201 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 ...
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?
5
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3answers
560 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
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2answers
103 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
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1answer
481 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
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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 ...
5
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1answer
284 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
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0answers
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 ...
4
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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 ...
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 ...
4
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4answers
397 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 ...
4
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1answer
199 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
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2answers
619 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
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1answer
46 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
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1answer
101 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
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2answers
32 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 (...
4
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1answer
118 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
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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 ...
4
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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, ...
4
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1answer
74 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
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1answer
146 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
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1answer
169 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
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1answer
302 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
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1answer
75 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
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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 ...
3
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2answers
47 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 ...
3
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1answer
170 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
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1answer
42 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
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1answer
24 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
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1answer
58 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
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1answer
46 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
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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 ...
3
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0answers
496 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
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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 ...
2
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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 ...
2
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1answer
231 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 ...
2
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1answer
62 views
+50

How is gradient being calculated in Andrej Karpathy's pong code?

I was going through the code by Andrej Karpathy on reinforcement learning using a policy gradient. I have some questions from the code. Where is the logarithm of the probability being calculated? ...
2
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1answer
48 views

When and how to use a mix of loss functions for back-propagation?

I am trying to understand the best loss function to be used with a convolutional neural network. I came to know that we can mix two loss functions. Can any body share in what case was it done and how?
2
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1answer
36 views

How is a neural network where the majority of inputs are 0 trained?

Take alexnet. Alexnet has 1000 output nodes, each of which classify an image: The problem I have been having with training a neural network of similar proportions, is that it does what any reasonable ...
2
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1answer
97 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 ...
2
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1answer
45 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 ...