softmax Questions
3
Solved
This is the screen of the original paper: the screen of the paper. I understand the meaning of the paper is that when the value of dot-product is large, the gradient of softmax will get very small....
Ningpo asked 27/2, 2019 at 12:42
2
I'm trying to find approach to compute the softmax probability without using exp().
assume that:
target: to compute f(x1, x2, x3) = exp(x1)/[exp(x1)+exp(x2)+exp(x3)]
conditions:
1. -64 < x1...
Settlement asked 4/6, 2020 at 8:25
5
Solved
I know that logistic regression is for binary classification and softmax regression for multi-class problem. Would it be any differences if I train several logistic regression models with the same ...
Toadinthehole asked 17/3, 2016 at 4:8
1
Solved
I'm trying to implement a Softmax activation that can be applied to arrays of any dimension and softmax can be obtained along a specified axis.
Let's suppose I've an array [[1,2],[3,4]], then if I...
Abdominal asked 24/9, 2022 at 11:11
2
I am trying to do sentiment analysis on a german tweet-data-set with the bert-base-german-cased modell which i imported over transformers from hugginface.
To be able to calculate the predicted pro...
4
Solved
I am watching some videos for Stanford CS231: Convolutional Neural Networks for Visual Recognition but do not quite understand how to calculate analytical gradient for softmax loss function using n...
2
I'm trying to implement the softmax function for a neural network written in Numpy. Let h be the softmax value of a given signal i.
I've struggled to implement the softmax activation function's ...
Cherey asked 29/3, 2016 at 9:7
2
I tried to find documents but cannot find anything about torch.softmax.
What is the difference among torch.nn.Softmax, torch.nn.funtional.softmax, torch.softmax and torch.nn.functional.log_softmax?...
1
I have a Tensorflow multiclass classifier that is generating nan or inf while computing probabilities using tf.nn.softmax. See the following snippet (logits is of shape batch_size x 6, since I have...
Lazaro asked 30/8, 2021 at 18:35
3
I am currently trying to reproduce the results of the following article.
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
I am using Keras with the theano backend. In the article he talks ab...
Koblenz asked 16/5, 2016 at 2:50
4
I know how to make softmax stable by adding to element -max _i x_i. This avoids overflow and underflow.
Now, taking log of this can cause underflow. log softmax(x) can evaluate to zero, leading to...
Raptorial asked 20/5, 2017 at 1:27
2
Given:
x_batch = torch.tensor([[-0.3, -0.7], [0.3, 0.7], [1.1, -0.7], [-1.1, 0.7]])
and then applying torch.sigmoid(x_batch):
tensor([[0.4256, 0.3318],
[0.5744, 0.6682],
[0.7503, 0.3318],
[0.24...
10
Solved
In the output layer of a neural network, it is typical to use the softmax function to approximate a probability distribution:
This is expensive to compute because of the exponents. Why not simpl...
Giraudoux asked 19/6, 2013 at 9:20
1
Solved
Given input like:
tensor([[[1.9392, -1.9266, 0.9664],
[0.0000, -1.9266, 0.9664],
[0.0000, -0.0000, 0.9664]]])
My desired output is:
tensor([[[0.4596, 0.0096, 0.1737],
[0.0000, 0.0096, 0.1737],
...
5
Solved
For a neural networks library I implemented some activation functions and loss functions and their derivatives. They can be combined arbitrarily and the derivative at the output layers just becomes...
Adrienadriena asked 5/11, 2015 at 10:16
2
Solved
I am trying to understand a simple implementation of Softmax classifier from this link - CS231n - Convolutional Neural Networks for Visual Recognition. Here they implemented a simple softmax classi...
Harlanharland asked 27/8, 2015 at 20:23
24
Solved
From the Udacity's deep learning class, the softmax of y_i is simply the exponential divided by the sum of exponential of the whole Y vector:
Where S(y_i) is the softmax function of y_i and e is t...
Catechism asked 23/1, 2016 at 20:52
2
Solved
Suppose I have the following tensor t as the output of a softmax function:
t = tf.constant(value=[[0.2,0.8], [0.6, 0.4]])
>> [ 0.2, 0.8]
[ 0.6, 0.4]
Now I would like to convert this matri...
Rochette asked 29/7, 2016 at 8:53
2
Solved
For any 2D tensor like
[[2,5,4,7],
[7,5,6,8]],
I want to do softmax for the top k element in each row and then construct a new tensor by replacing all the other elements to 0.
The result shoul...
Neurasthenic asked 13/11, 2018 at 12:20
2
Solved
Which dimension should softmax be applied to ?
This code :
%reset -f
import torch.nn as nn
import numpy as np
import torch
my_softmax = nn.Softmax(dim=-1)
mu, sigma = 0, 0.1 # mean and standa...
Cockney asked 26/9, 2018 at 8:59
1
Solved
I am trying to implement a Softmax Cross-Entropy loss in python. So, I was looking at the implementation of Softmax Cross-Entropy loss in the GitHub Tensorflow repository. I am trying to understand...
Upend asked 2/5, 2020 at 11:39
5
Solved
Most examples of neural networks for classification tasks I've seen use the a softmax layer as output activation function. Normally, the other hidden units use a sigmoid, tanh, or ReLu function as ...
Kami asked 2/6, 2016 at 10:1
1
Solved
So I'm struggling to understand some terminology about collections in Pytorch. I keep running into the same kinds of errors about the range of my tensors being incorrect, and when I try to Google f...
4
Solved
Is there a numerically stable way to compute softmax function below?
I am getting values that becomes Nans in Neural network code.
np.exp(x)/np.sum(np.exp(y))
Fountainhead asked 4/3, 2017 at 18:11
6
Solved
When I use this it does not give any error
out_layer = tf.add(tf.matmul(layer_4 , weights['out']) , biases['out'])
out_layer = tf.nn.softmax(out_layer)
But when I use this
model=Sequential()
...
Todd asked 9/6, 2018 at 16:59
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