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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binary cross entropy loss - pixels wise predictions - How error is back propagated?

I relate for example in a semantic segmentation task of picture 100 by 100 pixels. The task is to segment the picture using a mask for each picture as the label. Mask has values 0 and 1. (re. Unet). I ...
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Backpropagation incorrect only for hidden layer bias node

I am trying to implement a neural network in Python, however I am having some trouble when it comes to the implementation of backpropagation. I have been checking my results with this example and ...
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How CNN applies backpropagation to update its weights and biases?

I understand that the 3 main layers for CNN are convolutional layer, ReLU layer and pooling layer. However, I do not understand how CNN updates its weights and biases using backpropagation. I ...
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Can I update weights of keras neural net only if validation improves?

I am training a neural network in keras and I reach a classical limit - my training accuracy improves with increasing epochs, but my validation accuracy decreases after 9 epochs (see figure). I ...
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LSTM loss function and backpropagation

I'm trying to understand the connection between loss function and backpropagation. From what I understood until now, backpropagation is used to get and update matrices and bias used in forward ...
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Backpropagation: Relevance of the error signal of a neuron

During my quest to understand back propagation in a more rigorous approach I have come across with the definition of error signal of a neuron which is defined as follows for the $j^{\text{th}}$ neuron ...
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Backpropagation with log likelihood cost function and softmax activation

In the online book on neural networks by Michael Nielsen, in chapter 3, he introduces a new cost function called as log-likelihood function defined as below $$ C = -ln(a_y^L) $$ Suppose we have 10 ...
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Effect of ReLu derivative in convolution layer backpropagation

I'm trying to implement a CNN, as part of an academic project to learn how it works. The project is a SRCNN: a convolutional neural network that increase the resolution of images. Following this ...
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How to backpropogate error from convolutional layer with respect to the input when using multiple channels

I have been attempting to implement a Convolutional Neural Network in python and have run into a bit of a roadblock. When backpropogating the error in a convolutional layer let us say that we receive ...
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How does the Backpropagation through time work?

I have to write a paper on LSTMs and I want to explain why LSTMs exist in the first place. According to some papers and books because usual RNNs had problems with vanishing gradients and the LSTM has ...
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Forward and backward pass in Conv2D transpose Layer

I’ve several questions regarding the transposed convolution 2d layer. I’ve not been able to find a proper resource explaining the forward and backward pass. What I know (but not for sure) is, that ...
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BPTT vs Vanishing Gradient Problem

I know that BPTT is the method to apply Back Propagation on RNN. Which is works fine with RNN as it stops at certain point as changes approach to zero but isn't it the exact Vanishing Gradient ...
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Backpropagation Max-Pooling Layers: Multiple Maximum Values

I am currently implementing a CNN in plain numpy and have a brief question regarding a special case of the backpropagation for a max-pool layer: While it is clear that the gradient with respect to ...
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Backpropagation in a convolutional neural network with stride and padding

So i am trying to learn backpropagation of convolutional neural networks. A lot of articles only cover convolutions without a stride and a padding variable, so i decided to try it on my own. For ...
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Computing derivatives for backpropagation across a convolution step

This will be a long post, but I hope it'll be instructive to anyone else in my position. I'm trying to find how the derivatives of the loss function are calculated with respect to the kernels and ...
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Reason behind the sum of rate factors for calculating cost function derivative

Suppose we have a network of neurons like below: We make a little change in weight w[l][j][k] on our network, and it can make change on our cost function from ...
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What are the main reasons which does not cause the training error of yolov2 to not diminish?

I am using https://github.com/thtrieu/darkflow yolov2 for detecting and classifying the images of passport. There are 8 classes and all the objects are passport details like name, father name, mother ...
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Does a max-pooling layer in a ConvNet contribute to the “vanishing gradient” problem?

I would answer no, but am not sure if I'm missing something and hope you can help me out: The derivative of a max-pooling layer in a ConvNet is one w.r.t. the maximum value and zero for all others. A ...
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Chaining bias terms in backprob

let's say I have a few linear layers $l_1 \dots l_n$: $y=I(\dots I(IX + b_1) + b_2) \dots +b_n)$ where $n$ is sufficiently large and $I$ is the (nonparametric) identity matrix. The gradient for $b_n$...
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Is the Cross Entropy Loss important at all, because at Backpropagation only the Softmax probability and the one hot vector are relevant?

Is the Cross Entropy Loss (CEL) important at all, because at Backpropagation (BP) only the Softmax (SM) probability and the one hot vector are relevant? When applying BP, the derivative of CEL is the ...
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Neural network is getting partially trained

So I am writing my own neural network library using back-propagation as my training algorithm. Everything seems fine the error is getting decreased more and more at each iteration however when I am ...
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Max pooling has no parameters and therefore doesn't affect the backpropagation?

I feel this is a question that has a lot of variations already posted but it doesn't exactly answer my question. I understand the concept of max pooling and also the concept of backpropagation. What i ...
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How do I convert a summation equation to a vector equation (backpropagation)?

$$a_j^l=\sigma(\sum_{k} {w_j}_k^l {a}_k^{l-1}+b_j^l)$$ $$a^l=\sigma( w^l {a}^{l-1}+b^l)$$ In a resource I have been reading, the above equations describe the activation of a neurone. They have the ...
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Confused with the derivation of the gradient descent update rule

I have been going over some theory for gradient descent. The source I am looking at said that the change in cost can be described by the following equation: $$∆C=∇C∙∆w$$ where $∇C$ is the gradient ...
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Differences between gradient calculated by different reduction methods in PyTorch

I'm playing with different reduction methods provided in built-in loss functions. In particular, I would like to compare the following. The averaged gradient by performing backward pass for each loss ...
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Layer notation for feed forward neural networks

Apologies in advance, for I have a fairly rudimentary question on the notations for studying Feed-Forward Neural Networks. Here is a nice schematic taken from this blog-post. Here $x_i = f_i(W_i \...
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What does this expression from gradient descent mean?

I am looking over some neural network theory and came across this equation, coupled with this description (gradient descent ball-valley analogy): ''let's think about what happens when we move the ...
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Having problem in back propagation part for dimension

I was trying to build a neural network with single hidden layer from scratch. In back propagation part some problems have raised. For calculating gradient of loss function with respect to weight in ...
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LeCun paper on deeplearning (Nature, 2015)

As I was reading Y. LeCun's paper on Deep Learning (Nature, vol. 521, 2015), I came across a figure (the 1st one in the paper) which was associated to the backward ...
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Back propagation on matrix of weights

I am trying to implement a Neural Network for binary classification using python and numpy only. My network structure is as follows: input features: 2 [1X2] matrix Hidden layer1: 5 neurons [2X5] ...
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1answer
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Dueling Network gradient with respect to Advantage stream

Looking at Dueling DQN: $Q = V + A - mean(A)$ For simplicity, let's assume we are working with 4 neurons. Recall that Value stream only has 1 neuron $(v_0)$ Re-writing the above equation, we get: $...
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Is backpropogation used in convolutional neural networks?

Do convolutional neural network use the backpropogation algorithm? I am not understanding what exactly happens in fully connected layers?
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Get derivatives from your NN

How can I get the gradient of a node in the NN with respect to another one? I need to train a NN, which for the sake of simplicity has 2 neurons as input (x, y), a neuron as a bottleneck (z), and 2 ...
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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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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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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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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 ...