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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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My TD-backprop algorithm doesn't work

In the previous discussion I have tried to solve the TTT game with Q-learning with tables. Now I have tried to use Neural Network like function approximator and following these articles (for game of ...
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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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Backpropagation through time - How many Layers will an unfold produce?

In terms of Recurrent Neural Networks a backpropagation through time is used. That means, a RNN oder LSTM layer in Keras will be unfolded to x layers and backpropagation is performed on this unfolded ...
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Why CNN and Neural network implementation not working properly

I am working on implementation of Bangla Handwriting Recognition From Scratch. The major steps involved are as follows: Reading the input image. each image shape ( 100,100,3) Number of Train ...
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Unable to converge in my multi layer neural network while training on MNIST

I have been trying to implement neural network from scratch using numpy library only.... I have checked thoroughly and the net is able to converge in very simple dataset( 2d graph ) but I wanted to ...
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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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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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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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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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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 each weight is an expectation. In other words, to get this gradient $\nabla_{\theta} J_{\theta}$, we sample N ...
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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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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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1answer
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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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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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General equation - calculating backpropagation [closed]

How to calculate new weights for neurons - what is the general equation for it?
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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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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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Combining ARIMA and BackPropogation for CPU Utilization

I have to implement a model combining both ARIMA and BP to predict CPU Utilization based on previous data and reduce error. After implementing this ARIMA model I'm getting MSE of 1.6. I want to reduce ...
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29 views

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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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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Keras update Layers which are only Part of the Loss Function

I'm implementing a variational inference model. There are two main structures a inference network and a generative model. The first part is only used for training and only appears in the loss function....
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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
38 views

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 : ...
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“residual error” of LSTM during backprop vs usual “error”

What does the residual error mean when we are talking about LSTM? Taken from the middle of section 3 of this paper, where it says: "...of the residual error $\epsilon$" Where $s_0$ is the initial ...
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1answer
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Conflicting directions of weights gradients in gradient descent?

In a typical ANN backpropagation setting, we have multiple weights and we try to reduce the loss function by calculating the gradient of the function with respect to the weights let's say w1, w2, w3 ...
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Custom crop layer derivatives

I implemented a heuristic for cropping score maps (Caffe). The illustration is as follows. The cropped score map is an input in a fully connected layer. Current implementation of backprop in the ...
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2answers
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Python3 Tensorflow: Difference Between truncated_normal and uniform_normal when creating weights

So, I am new to using Tensorflow, and I am trying to create a neutral network, and in all the guides I saw one of the below lines of code was used. ...
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Should the partials be averaged once or multiple times for backprop

I am trying to make a general purpose symbolic differentiation library, and I am not sure how to handle mini batch learning for SGD. When back-propagating, the sums of the partials are usually added ...
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Backpropgating error to emedding matrix

I understand the backpropagation algorithm of neural networks, and how the error propagates backwards in layers. That is, I understand that given a 3-layer feed forward network, the amount to change ...
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1answer
153 views

Backpropagation - softmax derivative

I have a question on the backpropagation in a simple neural network (I am trying to derive the derivative for the backpropagation). Suppose that the network is simple like so (forward pass): $$\...
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(Deep Learning) Backpropagation derivation from notes by Andrew NG

I am self-studying Andrew NG's deep learning course materials from the mcahine learning course (CS 229) of Stanford. The material is available here: http://cs229.stanford.edu/notes/cs229-notes-...
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Pytorch: Combining Automatic and Manual Methods

I am testing a two-step architecture that is composed of a conventional first section that can be implemented with any standard deep learning architecture and a second section that must be coded ...
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Alternative method for RNN backpropagation through time

I am experimenting with a character-level LSTM model doing the standard task of predicting the next character given a sequence of characters. I am training the network using truncated backpropagation ...
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Synthetic Gradients - doesn't seem beneficial

I can see two motives to use Synthetic Gradients in RNN: To speed up training, by imediately correcting each layer with predicted gradient To be able to learn longer sequences I see ...
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Could someone explain to me how back-prop is done for the generator in a GAN?

I'm not very familiar with neural networks, however, I though I understood the concept of back propagation as starting from the error in the output layer. Say, we have 3 neurons in the output layer ...
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Synthetic Gradients good number of Layers & neurons

I would like to train my LSTM with a "synthetic gradients" Decoupled Neural Interface (DNI). How to decide on the number of layers and neurons for my DNI? Searching them by trial end error or ...
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How to understand backpropagation using derivative [duplicate]

Before that I was learning about gradient descent and I understand this, it's ok for me. Now I have a problem with backpropagation algorithm. I know the idea - minimalize error in multilayer neural ...
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Hochreiter LSTM (p. 4): Maximal values of logistic sigmoid derivative times weight

My questions follow the below page 4 excerpt from Hochreiter's LSTM paper: If $f_{l_{m}}$ is the logistic sigmoid function, then the maximal value of $f^\prime_{l_{m}}$ is 0.25. If $y^{l_{m-1}}$ ...
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neural network xor gate classification

I've written a simple neural network that can predict XOR gate function. I think I've used the math correctly, but the loss doesn't go down and remains near 0.6. Can anyone help me find the reason why?...
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Big Difference in Training from different output scaling methods

I am training an AC model on Pendulum-v0. After some very, very frustrating weekends and lots of gridsearch on params, I have found that one place in particular has made all of the difference. This ...
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1answer
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Can you sum up gradients and apply in batch?

I'm following this Reinforcement Learning Tutorial. There, training data is collected during an episode. When the episode is done, the data is used to do backpropagation. However, instead of applying ...
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1answer
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GradientChecking, can I blame float precision?

I am trying to GradientCheck my c++ LSTM. The structure is as follows: output vector (5D) Dense Layer with softmax (5 neurons) LSTM layer (5 neurons) input vector (5D) My gradient ...
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Why does momentum need learning rate?

If momentum optimizer independently keeps a custom "inertia" value for each weight, then why do we ever need to bother with learning rate? Surely, momentum would catch up its magnutude pretty ...