# Questions tagged [backpropagation]

For questions about the back-propagation (aka "backprop", and often abbreviated as "BP") algorithm, which is used to compute the gradient of the objective function (e.g. the mean squared error) with respect to the parameters (or weights) of the neural network, when trained with gradient descent.

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### Backpropagation not working as expected

I'm new to neural networks and I try to make a model that is guessing if a point is below or above relative to a function output. The idea is inspired from this video https://youtu.be/DGxIcDjPzac . ...
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### Gradient of Scalar objective cannot be efficiently calculated?

Suppose we generate the vector output $y$ from model $h(x, \theta)$, with input $x$ and parameters $\theta$. Reverse mode differentiation says that we can calculate the gradient \begin{align*} \...
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### Are there any good references that describe the equations of the forward pass of Graph Neural Networks?

I am trying to program Graph Neural Network from scratch. Can the community please suggest a good reference/s to read about the equations of the forward pass in Graph Neural Networks, especially in ...
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### Why is the error curve of a neural network trained with MSE to output $\frac{3 I_1 + 5 I_2}{2}$ given inputs $I_1$ and $I_2$ oscillating weirdly?

I just "finished" my first AI program. I programmed in Excel VBA, and I think it works well. I was checking every formula and the whole algorithm several times to make sure every formula is ...
29 views

### Positive bias causes the calculation of incorrect gradients

I have a data set with a positive bias (an image, where the values range from 0 to 1), that seems to be causing my network to calculate incorrect gradients. If I just use the raw image as input, of ...
119 views

### How to perform back-propagation in Decoupled Neural Interfaces?

I am attempting to create a fully decoupled feed-forward neural network by using decoupled neural interfaces (DNIs) as explained in the paper Decoupled Neural Interfaces using Synthetic Gradients (...
74 views

### Bias gradient of layer before batch normalization always zero

From the original paper and this post we have that batch normalization backpropagation can be formulated as I'm interested in the derivative of the previous layer outputs $x_i=\sigma(w X_i+b)$ with ...
48 views

### Why is Openai's PPO2 implementation differentiable?

I'm trying to understand the concept behind the implementation of the OpenAI PPO2 algorithm. The loss function that is minimized is as follows: ...
223 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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### Why is tf.abs non-differentiable in Tensorflow?

I understand why tf.abs is non-differentiable in principle (discontinuity at 0) but the same applies to tf.nn.relu yet, in case of this function gradient is simply set to 0 at 0. Why the same logic is ...
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### Are all activation functions in a layer same? [duplicate]

I understand that for you can have multiple activation functions in different layers. CNN's usually have Relu followed by softmax for the classification. But what stops us in having multiple ...
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### Why are the weights of the previous layers updated only considering the old values of the weights of the later layer, not the updated values?

Why are the weights of a neural net updated only considering the old values of the later layer, not the already updated values? I use this example to explain my problem. When applying the ...
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### Why does sigmoid saturation prevent signal flow through the neuron?

As per these slides on page 35: Sigmoids saturate and kill gradients. when the neuron's activation saturates at either tail of 0 or 1, the gradient at these regions is almost zero. the gradient and ...
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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 an XOR gate. I'm considering a neural network to be good if it can produce all the expected outcomes with the lowest ...
739 views

### In deep learning, is it possible to use discontinuous activation functions?

In deep learning, is it possible to use discontinuous activation functions (e.g. one with jump discontinuity)? (My guess: for example, ReLU is non-differentiable at a single point, but it still has a ...
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### What are the learning limitations of neural networks trained with backpropagation?

In 1969, Seymour Papert and Marvin Minsky showed that Perceptrons could not learn the XOR function. This was solved by the backpropagation network with at least one hidden layer. This type of network ...
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### How to compute the gradient of the cross-entropy loss function with respect to the parameters with softmax activation function?

I've seen plenty of examples of people doing Sigmoid + MSE backpropagation implementations, yet I do not seem to understand how to implement backpropagation as stated in the title in the case of multi-...
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### How does the neural-network know how to tweak weights for a specific neuron?

I know backpropagation uses cost and gradient descent to tweak the weights to minimize the cost. But how does it know which weights to give more weight to in the first place? Is there something inside ...
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### Do we know what the units of neural networks will do before we train them?

I was learning about back-propagation and, looking at the algorithm, there is no particular 'partiality' given to any unit. What I mean by partiality there is that you have no particular ...
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### Why is my derivation of the back-propagation equations inconsistent with Andrew Ng's slides from Coursera?

I am using the cross-entropy cost function to calculate its derivatives using different variables $Z, W$ and $b$ at different instances. Please refer image below for calculation. As per my knowledge, ...
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### Neural Network trains towards 1 despite target

So I'm trying to make my first neural network and have just finished my back propagation functions. I got the algebra from brilliant and thought I'd understood it, but my bug proves otherwise. The bug ...
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### During Backpropagation in LSTM, why is the previous output $h_{t-1}$ considered constant w.r.t any $W$ while computing derivative?

I've just started learning LSTM, and some points in the process of calculating the gradients are getting me confused. Say, for example, we want to compute $\frac{\partial}{\partial W_i}L$, where $L$ ...