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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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Reinforcement learning cost function [migrated]

Newb question I am writing a OpenAI Gym pong player with TensorFlow and thus far have been able to create the network based on a random initialization so that it would randomly return to move the ...
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1answer
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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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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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1answer
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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 ...
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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 ...
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1answer
44 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
60 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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Which Neuron represent which part of the non-linear feature?

In any neural network, each neuron in the network represents some part of non-linear feature of the input. Ex: Like in mnist data, Consider the stem of number 9 is cut into multiple pieces and ...
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1answer
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Training the generator in a GAN pair with back propagation

For the purposes of this question I am asking about training the generator, assume that training the discriminator is another topic. My understanding of generative adversarial networks is that you ...
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1answer
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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
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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
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Clarification on a backpropagation equation

so, I am currently Looking at different documents to understand backpropagation, mainly at this document. Now, at page 3, there is the Epsilon Symbol involved: While I understand the main part of 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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1answer
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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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4answers
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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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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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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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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
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How to perform neural network with output constraint?

Imagine a "simple" feedforward, fully connected neural network, with some input size, some number of hidden layers, and some # of neurons....etc BUT with a fixed number of output size (that is saying, ...
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1answer
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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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1answer
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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
120 views

How do I change the values of a neural net

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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1answer
759 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, ...
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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 ...
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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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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 ...
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1answer
119 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 ...
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1answer
211 views

Backward Pass for LSTMs

TL;DR I am currently trying to understand the mathematics in Ger's paper Long Short-Term Memory in Recurrent Neural Networks. I have found the document clear and readable so far. On pg. 21 of the ...
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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 ...
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1answer
557 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
38 views

How to change a weight/bias with gradient

After watching 3Blue1Brown's tutorial series, and an array of others, I'm attempting to make my own neural network from scratch. So far, I'm able to calculate the gradient for each of the weights and ...
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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 ...
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0answers
386 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
348 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
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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 ...
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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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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 ...
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1answer
173 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 much more about it. I have used mattmuzr's and DuttA's explanaiton as a guide to hand compute a simple neural network. I ...
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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 ...
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2answers
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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--...
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1answer
263 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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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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Data prepared to linear regression. Can I use it with backpropagation?

I'm studying a Master's Degree in Artificial Intelligence and I need to learn how to use the Java Neural Network Simulator, JavaNNS, program. In one practice I have to build a neural network to use ...
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1answer
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Is there any research on neural networks with multiple outputs for hierarchical label classification?

I had this idea of training for example a CNN on images, and having output branches at several of its intermediate layers. The early layers' output branch might then predict high-level class of ...
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1answer
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What are some concrete steps to deal with the vanishing gradient problem?

So 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 ...
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2answers
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what is the proof behind the gradient of a curve being equal/proportional to the distance between the two co-ordinates in the x-axis [closed]

In the delta rule the equation to adjust the weight with respect to error is :- where is the Learning Rate and E is the ...
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1answer
388 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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2answers
153 views

Why does ReLU (and other non linearities) work?

Can someone please point me to where I can read up on why non linearities that can produce values larger than 1 or smaller than 0 work. My understanding is that neurons can only produce values between ...
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2answers
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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 ...
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When do you back-propagate errors through a Neural Network when using TD Lambda

I have a Neural Network that I'm want to use to self-play Connect Four. The neural network receives the board state and is to provide an estimate of the states desirability. I would then, for each ...