# Tag Info

3

As far as I know, the sigmoid is often used as the activation function of the output layer mainly because it is a convenient way of producing an output $p \in [0, 1]$, which can be interpreted as a probability, although that can be misleading or even wrong (if you interpret it as an uncertainty too). You may require the output of the neural network to be a ...

11

This is a very important problem that is usually overlooked. In fact, when training a neural network, there's often the implicit assumption that the data is independent and identically distributed, i.e., you do not expect the data to come from a distribution different than the distribution from which your training data comes, so there's also the implicit ...

1

There are several ways you can do this. One is to input both images in input, so it can be a 2 input system or an input with 6 channels. As you suggested in 1st point, you can make 2 networks, connect them at the end and add another layer for final classification or use outputs from both and train another classifier (like Gradient bosting). You can look ...

1

This sounds to me like a use case for a chatbot. You would have different intents reflecting the types of user queries that your system can respond to. The intent matching can be done by pattern matching, machine learning (classification), or a combination of the two (hybrid). You can then use the chatbot to ask clarification questions or elicit more ...

1

It is much simpler to process the data in a different way. Since you're using temporal data a common practice is to define a priori a minimum time-step, usually called $\textit{granularity}$, which must be bigger than you're sensor responsiveness. Using this granularity value you'll then be able split your data into intervals, and you can then combine each ...

2

The main distinction between tasks is 'classification' vs 'regression'. In classification you would try to identify the presence of a cloud or not in an image, if you want to predict the level of 'cloudness' with continuous values you are then performing a regression task. I'm not aware about state-of-the models specific for images, but you can potentially ...

1

To offer a bit of theory, CNNs work well for many image tasks because they process spacially local information, without much care for absolute position. Essentially, every layer chops every image up into tiny crop images, and do an analysis step on the crops. The simple questions of "is this a line... corner... eye... face?" can be asked equally of every ...

0

The solution I reached after an hour of trial usually converges in just 100 epochs. Yeah, I know it does not have the smoothest decision boundary out there, but it converges pretty fast. I learned a few things from this spiral experiment:- The output layer should be greater than or equal to the input layer. At least that's what I noticed in the case of ...

2

Short answer is no. You can't use a model trained for one task to predict on a totally different task. Even if the second task was another image classification task, the CNN would have to be fine tuned for the new data to work. A couple of things to note... 1) CNNs are good for images due to their nature. It isn't necessary that they'd be good for any 2-...

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Obvious answer for a binary (2 classes)classification is .5. Beyond that the earlier comment is correct. One of the things I have seen done is to run your model on the test set and save the prediction probability results. Then create a threshold variable call it thresh. Then increment thresh from 0 to 1 in a loop. On each iteration compare thresh with the ...

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One problem could be with the selection of the validation set. For your model to work well on data it has not seen as training data is to have a high validation accuracy but that is not sufficient on its own. The validation set must be large enough and varied enough that its probability distribution is an accurate representation of the probability ...

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I have consistently found Adam to work very well but to tell you the truth I have not seen all that much difference in performance based on the optimizer. Other factor seem to have much more influence on the final model performance.In particular adjusting the learning rate during training can be very effective. Also saving the weights for the lowest ...

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There is a recent development in research that was looking into effectiveness of neural networks on arithmetic. Interestingly, feed-forward neural networks (MLPs) with various activation functions as well as LSTMs (RNNs which are Turing-complete) are not able to model simple arithmetic operations (e.g. addition/multiplication), they designed a new logic unit ...

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