# Tag Info

Accepted

### What does "e" do in the Sigmoid Activation Function?

The choice of $e$ is convenient when taking derivatives. Compare $\frac{d}{dx} \exp(x)$ to $\frac{d}{dx} a^x$ for any other $a > 0$.
• 453

### What does "e" do in the Sigmoid Activation Function?

If $d$ is a positive real number different from $1$, then $$d^{-x}=e^{-x\ln(d)}$$ So $d^{-x}$ is obtained from $e^{-x}$ by a horizontal shrink (when $\ln(d)>1$, that is $d>e$) or by a horizontal ...
• 151

### What does "e" do in the Sigmoid Activation Function?

To add to other answers: Note that the usefulness of $e$ as the base is not limited to this particular case of sigmoid activation function. It is the go-to base in so many areas of mathematics because ...
• 151

### Why is there tanh(x)*sigmoid(x) in a LSTM cell?

I think a better way to understand LSTMs is by their purpose, instead of gradients and distributions. If you analyze the interactions of each gate with the cell state, you'll realize that LSTMs ...
• 31
Accepted

### Why is there tanh(x)*sigmoid(x) in a LSTM cell?

The tanh functions within the cell represent cell output or cell state. These are the values that either get passed on to other layers, or within the layer to the next time step. In theory, other ...
• 30.2k
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### How do sigmoid functions make it so that the prediction $\hat{y}$ indicates the probability that the observed value, $y$, is $1$?

I am specifically asking about the probability that the value is 1 (that is, how sigmoid functions specifically check for this). They don't in general. In the quoted text, there is an explicit ...
• 30.2k
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### What happens when I mix activation functions?

The general answer to the behavior of combining common activation functions is that the laws of calculus must be applied, specifically differential calculus, the results must be obtained through ...
• 7,443

### Accuracy dropped when I ran the program the second time

It is common during the training of Neural Networks for accuracy to improve for a while and then get worse -- in general, This is caused by over-fitting. It's also fairly common for the Neural Network ...
• 1,094
Accepted

### Why is it a problem if the outputs of an activation function are not zero-centered?

Yes, if the activation function of the network is not zero centered, $y = f(x^{T}w)$ is always positive or always negative. Thus, the output of a layer is always being moved to either the positive ...
• 1,094
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### Can neural networks with a sigmoid as the activation function of the output layer approximate continuous functions?

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 ...
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### How can I train a neural network for another input set, without losing the learning of the previous input set?

Yes, this is actually a limitation known as catastrophic forgetting. A proposed way to deal with this is elastic weight consolidation that "remembers old tasks by selectively slowing down learning on ...
Accepted

### Network doesn't converge with ReLU or Leaky ReLU, but works well with sigmoid/tanh

I don't think it is dead ReLU units as a main cause, although they may be happening as part of the NN failing. The NN architecture is too complex for the given task (too deep, too many neurons) and ...
• 30.2k
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### Are ReLUs incapable of solving certain problems?

There are a variety of possible things that could be wrong, but let me give you some potentially useful information. Neural networks with ReLU activation functions are Turing complete for a ...
• 136

### Training a regression model on a set of values in 0-1 range to give 0-1 continual values

Many machine learning models used for regression will interpolate their predictions as you seem to want, and can return target values not seen in the training set. For example, basic linear regression ...
• 30.2k
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### Training a regression model on a set of values in 0-1 range to give 0-1 continual values

As long as you train the model with a proper loss function for regression the model will learn to output any continuous values, not restricted to and most likely not exactly equal to the labels your ...
• 5,218
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### Does it make sense to provide a DQN with negative rewards for a network with relu and sigmoid activations?

A network with ReLU activation can predict negative values; we put ReLU between the hidden layers but return the output of the final layer without any activation function, or with a linear activation ...
• 491
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### Target values of 0.1 for 0 and 0.9 for 1 for sigmoid

Derivative of the sigmoid curve is 0 when the output is 0 or 1 as you can see from the image above. The technique you are referring to is called label-smoothing which is used in various applications (...
• 106

### What does "e" do in the Sigmoid Activation Function?

$f$ is the unique function such that $f(0) = \frac{1}{2}$ and $f'(x) = f(x)(1 - f(x))$. Using $e$ is necessary to make sure the derivative takes this very simple form.
1 vote
Accepted

### How can a Regression based Neural Network learn class thresholds?

First thing to notice, is that the assumptions on the target don't match the ones of multi-classifications: in particular, in multi-class classification, it's generally assumed that any other class ...
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1 vote

• 39.5k
1 vote
Accepted

### Should I use additional empty category in some categorical problems?

In short: yes, you must allow "do nothing" decision as a first level result. Your system must decide the action to be taken, including "do nothing" action. This is different to low ...
• 1,283
1 vote

### Neural network doesn't seem to converge with ReLU but it does with Sigmoid?

It seems like you're suffering from the the dying ReLU problem. ReLU enforces positive values so the weights and biases your network learned are leading to a negative value passed through the ReLU ...

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