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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1answer
42 views

Confused with backprop in pytorch with BCE loss

I've a prediction matrix(P) of dimension 3x3 and one-hot encoded label matrix(L) of dimension 3x3 as shown below. ...
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10 views

How does the Bidirectional RNN works?

Could it be possible to reach a similar output via feeding a unidirectional network with the original data and the data played backwards?
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1answer
25 views

Backpropagation: Chain Rule to the Third Last Layer

I'm trying to solve dLoss/dW1. The network is as in picture below with identity activation at all neurons: Solving dLoss/dW7 is simple as there's only 1 way to output: $Delta = Out-Y$ $Loss = abs(...
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1answer
37 views

Why do very deep non resnet architectures perform worse compared to shallower ones for the same iteration? Shouldn't they just train slower?

My understanding of the vanishing gradient problem in deep networks is that as backprop progresses through the layers the gradients become small, and thus training progresses slower. I'm having a hard ...
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1answer
63 views

How can the derivative of a neural network be calculated, given no mathematical expression?

Neural networks (NNs) are used as approximators in reinforcement learning (RL). To update the policy in RL, the actor network's gradients w.r.t its weights are needed. Since NN doesn't have a ...
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1answer
23 views

Structure discrepancy of an LSTM?

I've found multiple depictions of how an LSTM cell operates. See 2 below: and Each of these images suggest the hidden state is utilised differently. On the top diagram, it is shown that the hidden ...
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0answers
27 views

How does a single neuron in hidden layer affect training accuracy

I'm currently a student learning about AI Networks. I've came across a statement in one of my Professor's books that a FFBP (Feed-Forward Back-Propagation) Neural Network with a single hidden layer ...
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49 views

How to train and update weights of filters

I have some problems with training CNN :( For example: Input 6x6x3, 1 core 3x3x3, output = 4x4x1 => pool: 2x2x1 By backpropagation I calculated deltas for output. This tutor and other tutors are ...
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1answer
56 views

How is REINFORCE used instead of Backpropagation?

In neural networks with stochastic layers I've seen the use of the REINFORCE estimator for estimating the gradient (because it can't be computed directly). Some such examples are Show, Attend and ...
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33 views

Not able to properly tune Neural Network via Back Propagation properly

I have a custom code Neural Network(not using keras or any package...Trying to learn the essence of Neural Network from scratch)... Code can be found here I have the per iteration training output(<...
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1answer
65 views

Backpropagation equation for a variant on the usual Linear Neuron architecture

Recently I encountered a variant on the normal linear neural layer architecture: Instead of $Z = XW + B$, we now have $Z = (X-A)W + B$. So we have a 'pre-bias' $A$ that affects the activation of the ...
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1answer
45 views

What weights should I use while back-propagating?

I've started to learn about neural networks recently and I can't find the answer to this question. Let's assume there's a neural network (fig. 1) So if the loss function is: and the derivative is: ...
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How can I use one neural network for both players in Alpha Zero (Connect 4)?

First of all, it is great to have found this community! I am currently implementing my own Alpha Zero clone on Connect4. However, I have a mental barrier I cannot overcome. How can I use one neural ...
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1answer
36 views

How is a neural network where the majority of inputs are 0 trained?

Take alexnet. Alexnet has 1000 output nodes, each of which classify an image: The problem I have been having with training a neural network of similar proportions, is that it does what any reasonable ...
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35 views

Confused about NeuralODE

I am a bit confused about NeuralODE and I want to make sure that what I understood so far is correct. Assume we have (for simplicity) 2 data points $z_0$ measured at $t_0$ and $z_1$ measured at $t_1$...
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1answer
46 views

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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0answers
51 views

Updating biasses while backpropagation in all details

I just wabt to ask do i understand the usage and updating biasses while backpropagating and what values comes where. Let us see following network : with bach size 6, input size 2, hidden size 4 and ...
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3answers
75 views

How does adding a small change to an neuron's weighted input affect the overall cost?

I was reading the following book: http://neuralnetworksanddeeplearning.com/chap2.html and towards the end of equation 29, there is a paragraph that explains this: However I am unsure how the ...
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1answer
107 views

Do you need to store prevous values of weights and layers on recurrent layer while BPTT?

The Back propagation through time on recurrent layer is defined similar to normal one, means somethin like ...
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1answer
117 views

How are filters weights updated for a CNN?

I've been trying to learn backpropagation for CNNs. I read several articles like this one and this one. They all say that to compute the gradients for the filters, you just do a convolution with the ...
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0answers
37 views

Difficulties to implement the layer-wise relevance propagation in MATLAB

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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2answers
73 views

How should the values of the filters of a CNN change?

I wrote a convolutional neural network for the MNIST dataset with Numpy from scratch. I am currently trying to understand every part and calculation. But one thing I noticed was the "just positive" ...
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0answers
38 views

How are the weights between the input and hidden layer updated in a 3 layer neural network?

Consider a feed-forward neural network with one hidden layer. How are the weights between the input and hidden layer updated, after the weights between the hidden layer and output layer are updated?
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1answer
49 views

When is bias values updated in back propagation?

I am new to deep learning. I have doubts on modifying bias values during back propagation. My doubts are Does the back propagation algorithm modifies the weigh values and bias values in the same pass?...
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1answer
92 views

Is back propagation applied for each data point or for a batch of data points?

I am new to deep learning and trying to understand the concept of back propagation. I have a doubt on when the back propagation is applied. Assume that I have a training data set of 1000 images for ...
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1answer
142 views

Back propagation on Flatten Layer in CNN

I am making a NN library without any other external NN lib and is implementing the Flatten layer. I know the forward implementation of flatten layer but is the backward just reshaping it or not? If ...
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2answers
103 views

Are on-line backpropagation iterations perpendicular to the constraint?

Raul Rojas' Neural Networks A Systematic Introduction, section 8.1.2 relates off-line backpropagation and on-line backpropagation with Gauss-Jacobi and Gauss-Seidel methods for finding the ...
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1answer
86 views

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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102 views

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
38 views

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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2answers
58 views

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
89 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
137 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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2answers
76 views

Which neuron represents which part of the input?

In a neural network, each neuron represents some part of the input. For example, in the case of a MNIST digit, consider the stem of the number 9. Each neuron in the NN represents some part of this ...
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1answer
57 views

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
252 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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2answers
239 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
45 views

What is the use of the $\epsilon$ term in this back-propagation equation?

I am currently looking at different documents to understand back-propagation, 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
40 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
24 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
378 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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0answers
28 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
95 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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0answers
47 views

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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2answers
402 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
59 views

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
244 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
128 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
2k 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
74 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 ...