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Questions tagged [backpropagation]

Use for questions about Backpropagation, which is commonly used in training Neural Networks in conjunction with an optimization method such as gradient descent.

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CartPole v1 - Simple backprop with 1 hidden layer

I'm trying to solve the CartPole-v1 problem from OpenAI by using backprop on a one-layer neural network - while updating the model at every time step using State action values (Q(s,a)). I'm unable to ...
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Layer-wise relevance propagation for the input layer

I'm studying this argoument and it looks pretty clear to me. I was looking to the LRP $\alpha_1\beta_0$ implementation, that's equal to the Deep-Taylor decomposition. The idea is the following, start ...
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Implementation of Layer-wise relevant propagation in MATLAB from scratch

I'm having serious issues with the implementation of the LRP algorithm for neural networks in MATLAB. The challenge is to implement the equations correctly. I'm trying to implement the deep-Taylor $\...
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Derivation of backpropagation for Softmax

So, after a couple dozen tries I finally implemented a standalone nice and flashy softmax layer for my neural network in numpy. All works well, but I have a question regarding the maths part because ...
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convolutional neural network backpropagation difficulties

I am trying to program a CNN from scratch. So I finished the forward propagation and the backpropagation through the dense/fully-connected layer. But now I have to update somehow my filters. For the ...
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Is this an incorrect way of back-propagating error with matrices?

I was watching a public available video from Stanford (https://youtu.be/d14TUNcbn1k?t=2720) on the mathematics behind back propagation. They proposed a graph: that was then used as an example of back ...
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How to handle maxpool layer backpropagation with recurring max values in same position

Say I have a layer a: 3 4 2 1 5 0 8 6 4 The maxpool using 2x2 filter is: <...
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Backpropagation implementation help

I'm trying to implement Nokland's Direct Feedback Alignment in Python following his paper. Here's my implementation so far: ...
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What are the criteria for updating bias values in back propagation?

During back propagation, the algorithm can modify the weight values or bias values to reduce the loss. How does the algorithm decide whether it has to modify the weight values or bias values to ...
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My question is about dependency between hidden states for Back Propagation Through Time in RNN

In one video lecture, professor Ali Ghodsi of University of Waterloo says that the first node of S(t)(hidden state of RNN at time t) has an effect only on the first node of S(t+1)(hidden state of RNN ...
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127 views

CNN Back Propagation without Sigmoid Derivative

I'm new to CNN and trying to study some MATLAB sample codes (cause I need to know the internal calculation). I recently realized that the sample code I'm using doesn't multiply error by sigmoid's ...
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How does back propagation works through layers like maxpooling and padding? [duplicate]

I know back propagation takes derivatives (changing one quantity wrt other). But how this is applied when there is maxpooling layer in between two Conv2D layers? How it gains its original shape when ...
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Would traditional ML approaches benefit from a feedback loop (a la backprop)?

Would classical ML approaches such as SVMs, RF etc. benefit by having some sort of feedback mechanism (similar to backpropagation in Deep learning)? If so, how can one go about it? Is there any ...
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Backpropagation for noobs

I am trying to understand neural networks and how they work, by programming my own one from scratch in nodejs. Currently, i managed to build a network, that has weights, layers and neurons. I also ...
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1answer
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What do “compile” , “fit” and “predict” do in Keras sequential models?

I am a little confused between these two parts of Keras sequential models functions. May someone explains what is exactly the job of each one? I mean ...
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Pytorch: How to create an update rule the doesn't come from derivatives?

I want to implement the following algorithm, taken from this book, section 13.6: Here, the neural networks' outputs are $V(S, w)$ and $\pi(A|S,\theta)$, parameterized by $w$ and $\theta$ respectively....
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quest for online tutorials on CNN backprop

I'm looking for video or text tutorial materials on CNN backprop that show how weights (convolutional kernels and last layer weights) are trained. Anyone know?
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How does Gradient Descent and Backpropagation work together?

Please forgive me as I am new to this. I have attached a diagram trying to model my understanding of neural network and Back-propagation? From videos on coursera and resources online I formed the ...
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Backpropagation using only numpy

I've been trying to implement the backpropagation algorithm using only numpy, I've already done the Keras version, but when implementing the numpy version, the loss is diverging as seen in the image ...
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1answer
45 views

Understanding backprop for softmax

I'm looking on a given solution of the first assignment of cs231n course. Down below a snippet from the loss function. I don't really understand lines 140-143. Can ...
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1answer
34 views

Information about LTSM RNN backpropagation algorithm

I am attempting to make a LTSM RNN in python from scratch and I have completed the code for forward pass but I am struggling to find a clear outline of the equations I need to calculate to get the ...
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1answer
36 views

What is the difference between reconstruction vs backpropagation?

I was following a tutorial on understanding Restricted Boltzmann Machines (RBMs) and I noticed that they used both the terms reconstruction and ...
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Using machine learning algorithm to approximate a matrix? [closed]

I don't know if this is a good question. I would like to use machine learning to approximate a matrix. For example, a very simple one as \begin{align} A=\begin{pmatrix} 1 &0 \\ 0& 2 \end{...
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LSTM - divide gradients by number of timesteps IMMEDIATELY or in the end?

From this answer I know that the gradient of an average of many functions, is equal to the average of the gradients of those functions taken separately. The error gradient that you want to ...
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1answer
278 views

CNN backpropagation between layers

I have this CNN architecture: I know how to calculate error for weights based on the output and update weights between output<-->hidden and hidden<-->input layers. The problem is that I have ...
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1answer
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Do we need to add the sigmoid derivative term in the final layer's error value?

I have been studying professor Andrew Ng's Machine Learning course on Coursera. Currently, I am trying to prove the formulas for backpropagation, which is mentioned in Week 5 (in this document). ...
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Using multiple machine learning algorithms together [closed]

I'm kinda new to machine learning and wanted to know if we could use multiple machine learning algorithms, for example, SVM and backpropagation together to solve a particular problem.
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How can I perform backpropagation directly in matrix form?

I had made a neural network library a few months ago, and I wasn't too familiar with matrices. So, instead of performing matrix dot products (between weights and inputs, then adding a bias matrix), I ...
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An error with respect to filter weights in CNN during the backpropagation

Let's say a convolutional layer takes an input $X$ with dimensions of 5x100x100 and applies 10 filters $F$ 5x5x5, thus produces an output $O$ 10 feature maps 96x96. During the backpropagation the ...
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1answer
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has number of output layer of DNN any effect in speed of find the optimal answer of DNN?

has number of output layer of DNN any effect in speed of find the optimal answer of DNN? For instance the more episodes is needed to train a DNN when the number of outputs is more? Is it correct?
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370 views

Find partial derivative of softmax w.r.t logits in python

I have trouble implementing back propogation for multi class classification My neural network has 2 layers Forward propagation X -> L1 -> L2 weights W are initialized as random ...
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1answer
75 views

Backpropagation

I use chain rule when doing backpropagation and then I do Gradient Descent with weighting coefficient and I am updating the weight, so I do not understand how the method works in the equations below. ...
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1answer
93 views

Policy Gradients - gradient Log probabilities favor less likely actions?

Assume we work with neural networks, with the policy gradients method. The gradient w.r.t to the objective function $J$, is an expectation. In other words, to get this gradient $\nabla_{\theta} J(\...
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204 views

Purpose of backpropagation in neural networks

I've just finished conceptually studying linear and logistic regression functions and their optimization as preparation for neural networks. For example, say we are performing binary classification ...
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Understanding Timestamps and Batchsize of Keras LSTM considering Hiddenstates and TBPTT

What I'm trying to do What I am trying to do is predicting the next data-point $x_t$ for each point in the timeseries $[x_0, x_1, x_2,...,x_T]$ in the context of a date-stream in real-time, in theory ...
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1answer
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How do you generalize from stochastic to batch learning using gradient descent?

I understand pretty OK how to derive the formulas and implement stochastic gradient descent for a deep neural network (even though the total derivative magic for hidden layers is a bit pushing my ...
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Simplifying gradients of weights (RNN)

I understand that these are the gradients of the weights/biases in an RNN (correct me if I am wrong): This is a lot to compute and I’m aware that these equations can be simplified for ease of use. ...
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Backpropogation through Maxpool and relu

Why is backpropagation through maxpool and relu needed? Purpose of backpropagation is to update weights while on the other hand maxpool and relu only perform a simple operation on the input. They don'...
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1answer
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Inconsistency in training Iris dataset

I am a noob in the field of ML. I have been trying to classify the iris dataset. I managed to do it with backpropagation, and with 3 neurons in the hidden layer. But the mean square error that I get ...
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79 views

General equation - calculating backpropagation [closed]

How to calculate new weights for neurons - what is the general equation for it?
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138 views

Backpropagation - simplest explanation

Could you please explain in simplest way the algorithm (mathematical equation) of back-prop? I read lot of articles, I know for what it is, and I understand the intuition behind it, but I still don't ...
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How does backpropogation behave when there is a dropout in FC layer?

I read about dropout and how it helps in catering overfitting. In simple layman terms, it randomly drops some of the neurons in forward propagation. My question is that since these neurons will be 0 ...
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1answer
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Questions about Neural Network training (back propagation) in the book PRML (Pattern Recognition and Machine Learning)

I am reading Chapter 5 of PRML. Some symbols don't seem to be clear to me. In page 243, for the chain rule for partial derivative $\dfrac{\partial E_n}{\partial w_{ji}}=\dfrac{\partial E_n}{\partial ...
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Backpropogation “The hardest derivation I have seen in machine learning”?

In week 3 of Andrew Ng's "Neural Networks and Deep Learning" on Coursera there is a video titled "Backpropogation Intuition" during which he gives the full vectorized backpropogation formula, and ends ...
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Using neural networks to solve polynomials

Is it possible to train a neural network to solve a polynomial equation? What about any non-linear single variable equation? Here are possible methods that I am thinking of trying: Training a neural ...
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198 views

Backpropagation of convolutional neural network - confusion

I've already seen many articles about this topic and Backpropagation In Convolutional Neural Networks by Jefkine seems to be the best. Although, as author said, For the purposes of simplicity we ...
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141 views

Algorithm for backpropagation through time

I am reading through this article trying to understand the bptt algorithm, in the context of an RNN. However there is one part I don’t understand: ...
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Understanding the text from the paper 'Efficient BackProp' by Yann LeCun

Sorry, I just started in Deep Learning, so I am trying my best not to assume anything unless I am absolutely sure. Going through comments here someone recommended this excellent paper on ...
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How is error back-propagated in a multi-layer RNN

Let's say I have a 2 layer LSTM cell, and I'm using this network to perform regression for input sequences of length 10 along the time axis. From what I understand, when this network is 'unfolded', ...
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1answer
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Tensorflow Calculate error for a single neuron

I'm required to be able to calculate the error on a given neuron in a neural network using Tensorflow. Using this : ...