Normalizing exponential function which transforms a numeric vector such that all its entries become between 0 and 1 and together sum to 1. It is often used as the final layer of a neural network performing a classification task.
Questions tagged [softmax]
229 questions
9
votes
1 answer
What is the gradient-log-normalizer?
In the wiki, the softmax function is defined as the gradient-log-normalizer of the categorical probability distribution. A partial explanation to the log-normalizer is found here, but what does
gradient-log-normalizer stand for?
tashuhka
- 607
- 1
- 7
- 17
8
votes
3 answers
Soft version of the maximum function?
In the book Deep Learning, it says the softmax function is de facto a soft argmax function, and the corresponding soft version of the maximum function is $$\text{softmax} (z)^T z$$
How to understand the latter?
JP Zhang
- 241
- 2
- 11
4
votes
2 answers
Softmax variant based on rank of vector indices
Given a $K$-dimensional real-valued vector $\mathbf{z} = (z_1, z_2, \ldots, z_K)$, I know that the softmax function returns a vector $\sigma(\mathbf{z})$ with positive elements summing to 1 via the following formula:
$$
\sigma(\mathbf{z})_j =…
josliber
- 4,367
- 28
- 43
2
votes
1 answer
Why does deriving the softmax for a single vector come to 0 for me?
There is a lot of material explaining how to calculate the jacobian for the softmax backwards pass, but I find it confusing how to get to the actual errors from the jacobian. The obvious answer would be to sum up the either rows or the columns - it…
Marko Grdinić
- 131
2
votes
1 answer
How does softmax relate/backpropagate probabilities with binary label?
When the softmax is the last layer in the neural network, it usually takes input from a fully connected layer (say of size 1000) and then outputs probabilities of each of this output (if I understand it correctly).
Assuming that our task is to…
cerebrou
- 275
1
vote
1 answer
Confusion with softmax
I was wondering if someone could explain why, if I do softmax on
[683, 861, 981, 834]
I get
[3.80403403e-130 7.66764807e-053 1.00000000e+000 1.44115655e-064]
But if I take a factor of 100 out:
[6.832, 8.61, 9.81, 8.34]
then I get…
BigBadMe
- 434
1
vote
1 answer
Softmax where the max probability is less than one
I have a neural network with a softmax at the end.
Something like this:
def forward(self, x)
x = self.conv(x)
x = self.channel_transform_layer(x)
output = self.softmax(x)
return output
I would like the maximum value of the logits to…
0
votes
0 answers
Classification in the context of a time series
I would like to exchange with you about how to best do this.
Let me try to clearly describe the situation in the most non-verbose way I can!
(Stack: Python, Darts (from unit8), pandas, numpy)
I have a building temperature time series, I perform…
MDSv
- 33
0
votes
2 answers
Softmax derivation - case i==j
I am reading this article: https://eli.thegreenplace.net/2016/the-softmax-function-and-its-derivative about softmax derivation w.r.t. to the input. Please confirm my understanding of the case when j==i:
\begin{equation}
DjSi=\begin{cases}
…
artona
- 125
- 4