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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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 ...
18
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1answer
429 views
Are these two versions of back-propagation equivalent?
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
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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 ...
9
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2answers
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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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What is “backprop”?
What does "backprop" mean? Is the "backprop" term basically the same as "backpropagation" or does it have a different meaning?
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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 badge7 silver badges18 bronze badges
7
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3answers
663 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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1answer
367 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
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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 ...
6
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1answer
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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 $w_2$ ...
6
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2answers
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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 ...
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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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1answer
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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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2answers
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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
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2answers
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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
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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
540 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
171 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
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What kind of algorithm is the LevenbergāMarquardt algorithm?
Is a LevenbergāMarquardt 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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0answers
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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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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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3answers
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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
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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 ...
4
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1answer
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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
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1answer
215 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
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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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1answer
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What, exactly, does the REINFORCE update equation mean?
I understand that this is the update for the parameters of a policy in REINFORCE:
$$
\Delta \theta_{t}=\alpha \nabla_{\theta} \log \pi_{\theta}\left(a_{t} \mid s_{t}\right) v_{t}
$$
Where š£š” is ...
4
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1answer
68 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
111 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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1answer
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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
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2answers
55 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
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1answer
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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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1answer
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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
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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
350 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
153 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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2answers
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What should I do with the flatten layer during back-propagation?
I'm creating a CNN network without other frameworks such as PyTorch, Keras, Tensorflow, and so on.
During the forward pass, the Flatten layer reshapes the previous ...
4
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1answer
86 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
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0answers
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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
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1answer
92 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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3answers
324 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
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1answer
54 views
How can I use a Hidden Markov Model to recognize images?
How could I use a 16x16 image as an input in a HMM? And at the same time how would I train it? Can I use backpropagation?
3
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2answers
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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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3answers
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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 ...
3
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1answer
122 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
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1answer
238 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 ...
3
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1answer
229 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
109 views
How do I calculate the partial derivative with respect to $x$?
I am trying to implement CNN using python Numpy.
I searched so much, but all I found was for one filter with one channel for Convolution.
Suppose we have an X as Image with this shape: ...
3
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1answer
78 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
37 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 ...
