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Questions tagged [backpropagation]

For questions related to the technique of backpropagation, whereby the loss, error, or correction signal at an artificial neural network output is fed back to the sequence of network layer parameters that produced the output, until learning converges to within the required accuracy and reliability.

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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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Python LSTM RNN Back propagation doesn't pass gradient check

I am trying to code a Recurrent neural network in python and I am having trouble getting the back propagation step to correctly calculate the gradients as when I check it using gradient checking the ...
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1answer
26 views

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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Does Keras keep track of weights on backpropagation when slicing on custom Lambda layers?

The result of a convolution layer has the shape: [?, 16, 512], where the last dimension represents the filters. Then, I split the last dimension into 256 / 256, and perform a filter-wise dot-product, ...
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2answers
214 views

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
30 views

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
35 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 ...
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1answer
23 views

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
191 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 ...
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26 views

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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0answers
76 views

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
48 views

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
62 views

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
118 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
216 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
50 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 ...
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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
98 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
176 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
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 ...
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43 views

Back propagation in NN with sigmoid activation function - division by 0

I am following this article Mind: How to Build a Neural Network (Part One) to build 'hello world' in the world of neural networks - XOR. During the back-propagation phase author calculates changes in ...
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1answer
275 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
34 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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0answers
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Trying to Implement backpropagation algorithm in FNN Paper

I'm trying to implement the backpropagation algorithm in python as noted in this paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1016716 in Section 3. So far this is what I have: ...
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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
331 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
290 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
90 views

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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0answers
180 views

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
152 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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1answer
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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
238 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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Which is the best software for implement neural network with back propagation modified by PSO and GA?

I am working on my senior project about classification, using the neural network and backpropagation algorithm, and for my research I will modify the the backprop algorithm using combination of ...
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Clockwork RNN & Backpropagation logic on non-activated modules

Thanks for reading this post ! Quick question for RNN enthusiasts here : I know that in backproprgation through time (BPPT), there is at least 3 steps : For each element in a sequence : Step 1 - ...
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2answers
894 views

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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Backpropagation in ANFIS creating corrections to membership function parameters that are too large

I am trying to create a python library that implements ANFIS. The forward pass appears to be working fine, but I am struggling with the backward pass to tune the membership function parameters of a ...
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1answer
61 views

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
83 views

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
108 views

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
372 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
128 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
2k 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 ...
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0answers
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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 ...
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How to design 4D Deep recurrent neural networks in Tensorflow?

I want to design a simple model that predicts the movement of coordinates with RNN. In a typical three-dimensional LSTM model, one feature is encoded as one hot encoding, and the x value is input as ...
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380 views

Forecasting and predict using matlab Artificial Neural Network [closed]

I selected the below data set for forecast and predict using artificial neural network as my final year project. https://archive.ics.uci.edu/ml/datasets/Bank+Marketing. I normalized the data set and ...