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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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
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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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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 network ...
asked Aug 3 '16 at 18:05
S.L. Barth - Reinstate Monica
1,0841 gold badge7 silver badges19 bronze badges
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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 ...
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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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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 ...
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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 $w_2$ ...
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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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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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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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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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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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1answer
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Why is the change in cost wrt bias in neural network equal to error in the neuron?
While reading the book on neural networks by Michael Nielson, I had a problem understanding equation (BP3), which is
$$
\frac{\partial C}{\partial b_{j}^{l}}=\delta_{j}^{l} \tag{BP3}\label{BP3},
$$
...
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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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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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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 ...
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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 (...
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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 ...
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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
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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 ...
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1answer
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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
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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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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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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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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 ...
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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 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
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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
234 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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1answer
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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
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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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What should I do with the flatten layer during back-propagation? [duplicate]
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 ...
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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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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 ...
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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 ...
4
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1answer
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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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1answer
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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 ...
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1answer
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Why is my derivation of the back-propagation equations inconsistent with Andrew Ng's slides from Coursera?
I am using the cross-entropy cost function to calculate its derivatives using different variables $Z, W$ and $b$ at different instances. Please refer image below for calculation.
As per my knowledge, ...
4
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1answer
232 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 ...
4
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1answer
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What is asymmetric relaxation backpropagation?
In Chapter 8, section 8.5.2, Raul Rojas describes how the weights for a layer of a neural network can be calculated using a pseudoinverse of the sigmoid function in the nodes, he explains this is an ...
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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 ...
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0answers
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How can I formulate a nonogram problem as a constraint satisfaction problem?
I've just started learning CSP and I find it quite exciting. Now I'm facing nonogram solving problem and I want to solve it using backtracking with CSP.
The first problem that I face is that I cannot ...
4
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1answer
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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 ...
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0answers
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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
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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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2answers
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What exactly is averaged when doing batch gradient descent?
I have a question about how the averaging works when doing mini-batch gradient descent.
I think I now understood the general gradient descent algorithm, but only for online learning. When doing mini-...
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1answer
61 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?
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2answers
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Are filters fixed or learned?
No matter what I google or what paper I read, I can't find an answer to my question. In a deep convolutional neural network, let's say AlexNet (Krizhevsky, 2012), filters' weights are learned by means ...
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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 ...
