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Questions tagged [mean-squared-error]

For questions related to the mean squared error (MSE) function, which is often used to solve regression problems.

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How to optimise a FNN/MLP network with MSE (positive only loss) in C

I can create a FNN/MLP network in C but only g-p loss works, where g = ground truth and p = predicted. What I don't understand is how MSE a positive only loss value can train a back propagation ...
user avatar
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Loss function not able to capture the maxima of probability distribution

I am trying to predict noise (random gaussian) with the help of a neural network. I am implementing a L2 loss (torch.nn.function.mse_loss) for computing the loss function between the prediction ...
Formal_this's user avatar
2 votes
2 answers

Does MSE loss function work in NN training for predicting values between 0-1?

In a NN regression problem, considering that MSE is squaring the error and the error is between 0 and 1 would it be pointless to use MSE as our loss function during model training? For example: ...
Darren Rahnemoon's user avatar
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Which mathematical properties do PSNR and MSE hold?

We know the Structural Similarity Index (SSIM) holds the following properties: Unique maximum: S(x, y) = 1 if and only if x = y Boundedness: ...
Theo Deep's user avatar
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Is there any different evaluation metrics(Performance Metrics) for Deep learning ,Machine, learning and NLP?

I'm a little confused about machine learning. I know we can use accuracy, and precision-recall when it comes to a classification problem, and when it comes to regression problems, we usually go with ...
Shreneek Upadhye's user avatar
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1 answer

Multi-layer network only predicts linear trends

I have made a neural network from scratch (in java), which is refusing to switch out of linear regression. I have pushed up the layer sizes (it now has 2 hidden layers, both with 5 neurons), and yet ...
Gamaray's user avatar
  • 13
3 votes
2 answers

In variational autoencoders, why do people use MSE for the loss?

In VAEs, we try to maximize the ELBO = $\mathbb{E}_q [\log\ p(x|z)] + D_{KL}(q(z \mid x), p(z))$, but I see that many implement the first term as the MSE of the image and its reconstruction. Here's a ...
IttayD's user avatar
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"Porpoising" in latter stages of validation loss and MSE charts in Keras

Performing a prediction of a continuous y target using Keras, the simple structure of the code revolves around; ...
Will's user avatar
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1 answer

What error should I use for RNN?

I'm relatively new to machine learning, and I don't know what error I should use for an RNN. I want to use a simple Elman RNN to predict the cases of Covid-19 there will be in a hospital for the next ...
mariogarcia's user avatar
2 votes
1 answer

Would either $L_1$ or $L_2$ regularisation lower the MSE on the training and test data?

Consider linear regression. The mean squared error (MSE) is 120.5 for the training dataset. We've reached the minimum for the training data. Is it possible that by applying Lasso (L1 regularization) ...
user6394019's user avatar
1 vote
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How do I prove that the MSE is zero when all predictions are equal to the corresponding labels?

In the back-propogation algorithm, the error term is: $$ E=\frac{1}{2}\sum_k(\hat{y}_k - y_k)^2, $$ where $\hat{y}_k$ is a vector of outputs from the network, $y_k$ is the vector of correct labels (...
Slowat_Kela's user avatar
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What is the meaning of these equations in Noise2Noise paper?

I am trying to understand what is meant by following equations in the Noise2Noise paper by Nvidia. What is meant by the equation in this image? What is $\mathbb{E}_y\{y\}$? And how should I try to ...
Markov's user avatar
  • 41
-1 votes
1 answer

What is the definition of a loss function in the context of neural networks?

I have read what the loss function is, but I am not sure if I have understood it. For each neuron in the output layer, the loss function is usually equal to the square of the difference value of the ...
Se1fie's user avatar
  • 9
5 votes
2 answers

What is the advantage of using cross entropy loss & softmax?

I am trying to do the standard MNIST dataset image recognition test with a standard feed forward NN, but my network failed pretty badly. Now I have debugged it quite a lot and found & fixed some ...
Ben's user avatar
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1 vote
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How can a learning rate that is too large cause the output of the network (and the error) to go to infinity?

It happened to my neural network, when I use a learning rate of <0.2 everything works fine, but when I try something above 0.4 I start getting "nan" errors because the output of my ...
user1477107's user avatar
2 votes
1 answer

Why do we calculate the mean squared error loss to improve the value approximation in Advantage Actor-Critic Algorithm?

jgauth's user avatar
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How MSE should be appliead with multi target deep network?

I'm having a problem understanding how the MSE should be used when working with a multidimensional target, e.g 3 dimensiones. (My outputs are continuois values, not categorical) Let us say I have a ...
Cla's user avatar
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2 votes
2 answers

What does it mean if classification error is equal between two networks but the MSE is different?

I'm experimenting with training a feedforward neural network using a genetic algorithm and I've done a few tests using both the mean squared error and classification error functions as fitness ...
gator's user avatar
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2 votes
4 answers

Is it normal to have the root mean squared error greater on the test dataset than on the training dataset?

I am new to deep learning. I am training a model and I am getting a root mean squared error (RMSE) greater on the test dataset than on the training dataset. What could be the reason behind this? Is ...
Debugger's user avatar
2 votes
2 answers

How to determine the target value when using ReLU as activation function?

Consider the following simple neural network with only one neuron. The input is $x_1$ and $y_2$, where $-250 < x < 250$ and $-250 < y < 250$ The weights of the only neuron are $w_1$ and $...
Alan Vinícius's user avatar
4 votes
3 answers

In which cases is the categorical cross-entropy better than the mean squared error?

In my code, I usually use the mean squared error (MSE), but the TensorFlow tutorials always use the categorical cross-entropy (CCE). Is the CCE loss function better than MSE? Or is it better only in ...
Dan D.'s user avatar
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3 votes
2 answers

How to express accuracy of a regression ANN that uses MSE loss function?

I have a regression MLP network with all input values between 0 and 1, and am using MSE for the loss function. The minimum MSE over the validation sample set comes to 0.019. So how to express the '...
samiant's user avatar
  • 59
6 votes
2 answers

What are the advantages of the Kullback-Leibler over the MSE/RMSE?

I've recently encountered different articles that are recommending to use the KL divergence instead of the MSE/RMSE (as the loss function), when trying to learn a probability distribution, but none of ...
razvanc92's user avatar
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9 votes
3 answers

How is it possible that the MSE used to train neural networks with gradient descent has multiple local minima?

We often train neural networks by optimizing the mean squared error (MSE), which is an equation of a parabola $y=x^2$, with gradient descent. We also say that weight adjustment in a neural network by ...
isnvi23h4's user avatar
  • 213
4 votes
1 answer

Why is the "square error function" sometimes defined with the constant 1/2 and sometimes with the constant 1/m?

Depending on the source, I find people using different variations of the "squared error function". How come that be? Here, it is defined as $$ E_{\text {total }}=\sum \frac{1}{2}(\text {...
Sebastian Nielsen's user avatar
2 votes
1 answer

Is it a good idea to train a CNN to detect the hydration value (percentage) in skin images and evaluate it with the MSE?

I have a large dataset of skin images, each one associated with a hydration value (percentage). Now I'm looking into predicting the hydration value from an image. My thinking: train a CNN on the ...
Patrick Samy's user avatar