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3 votes
Accepted

Is it bad to average several MAEs calculated from chunks of a big test dataset?

Can I save the MAE from each chunk of data and then average them ? Yes. This is perfectly fine. Why? Think about the metric's definition. Caveat: We assume $k$ chunks of equal chunk size $cs$. If ...
J_H's user avatar
  • 1,130
2 votes

How to Findout if a neural network is invariant

What kind of neural network are you using? It could be invariant if the initial weights are always the same, but some NN can also have stochastic inner mechanisms (ex, noise and minibatches). In all ...
Nicolas Martin's user avatar
2 votes

What does it mean if a neural networks starts overfitting more after applying regularisation techniques

The results seems alright. Your training accuracy could be 99% if trained on enough epochs but it does not mean it is a real indicator on how well it will do on unseen data. Regularization bridges ...
Rathod's user avatar
  • 81
2 votes
Accepted

Train CNN weights by using FFT - Reinforcement Learning?

If you're doing this strictly for learning the inner machinery of how a CNN works, then whipping up something in C++ or python or your language of choice is fine, and can be a good learning exercise. ...
brewmaster321's user avatar
1 vote
Accepted

How to chose the right activation function for CNN output depending on the output value ranges?

If I use the sigmoid function it will have an upper-bound, but to do so I have to normalize my labels to the 0-1 range. My images are not squares, I was thinking about normalizing x and y coordinates ...
MuhammedYunus's user avatar
1 vote
Accepted

Applying dropout effectively in CNN

Firstly, why do we use dropout in the first place? Dropout is a regularization technique designed to improve generalization and prevent overfitting. With this in mind, you should not necessarily ...
Karl's user avatar
  • 756
1 vote

why validation accuracy is stuck at 75%?

My first inclination is that your dataset size is relatively small, and you have many hidden layers, which increases the likelihood of your model overgeneralizing on the training data, leading to ...
Daniel Curtis's user avatar
1 vote

How to align the description of a convolutional neural network in keras with wikipedia's conceptual model?

There are two misunderstandings: How Convolution with layers works How the pooling is defined Convolution with layers Let's start with the ominous 32: ...
Broele's user avatar
  • 1,535
1 vote

How to optimize transposed convolution?

I suggest using a bilinear interpolation followed by a convolution instead of deconvolution. Deconvolution is prone to checkerboard artifacts. It may also helps in terms of execution speed.
Lelouch's user avatar
  • 161
1 vote

Why the training accuracy stays high but validation accuracy does not change?

You are using medical images for which the difference in images might not be significant. For this, you can use residual networks or ResNet architecture which has residual connections which improves ...
Rathod's user avatar
  • 81
1 vote

Is improving a Neural Network really just "trial and error"?

Some activation functions work better in some cases. Hidden layers with ReLU work surprisingly well. I would usually recommend adding some regularization to generalize better. Use a variable learning ...
Yash Mali's user avatar
1 vote

Is improving a Neural Network really just "trial and error"?

I'm new to this field as well, and from what I've learnt, yes, a lot of what we're doing while working with NNs is empirical. We mostly build such NNs to work with some specific type of data, and ...
code's user avatar
  • 11
1 vote

Training ResNet50 model for binary classification

Static learning rate is usually a bad thing, and 0.1/0.08 is usually a very high learning rate after a few epoch (for some networks even 0.001 is too high). Easiest thing to do is going with Adam ...
Lelouch's user avatar
  • 161
1 vote
Accepted

What are the general rules or principles for finding matrix operations that are used as filters in convolutional neural networks?

In general, the filters of a convolutional network are NEVER designed, they are trained. Of course, there can be research lines that study the inclusion of hand-designed filters in CNNs, but in the ...
noe's user avatar
  • 27k

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