Answers.Body: <p>The main purpose of the <a href="https://en.wikipedia.org/wiki/Softmax_function" rel="nofollow noreferrer">softmax function</a> is to transform the (unnormalised) output of <span class="math-container">$K$</span> units (which is e.g. represented as a vector of <span class="math-container">$K$</span> elements) of a fully-connected layer to a probability distribution (a normalised output), which is often represented as a vector of <span class="math-container">$K$</span> elements, each of which is between <span class="math-container">$0$</span> and <span class="math-container">$1$</span> (a probability) and the sum of all these elements is <span class="math-container">$1$</span> (a probability distribution). </p>
  
  <p>In the case of a classification task, the <span class="math-container">$i$</span>th element of the vector produced by the softmax function corresponds to the probability of the input of the network of belonging to the <span class="math-container">$i$</span>th class (e.g. a dog).</p>
  
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Answers.Text: but can it be replaced to argmax function (taking element with higher value as 1, everythin else is 0)
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Answers.Text: @user8426627 You could do that, but you might lose the probabilistic interpretation of the results (classification). At the end, you will have to make a decision, so you will choose one (or more) of those outputs (anyway). The most obvious decision is to choose the class with the highest probability, but this might not always be the case.
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Answers.Text: i just want to figure out, does softmax gains benefit when packpropagating from it compare to just use linear difference
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Answers.Text: @user8426627 I'm not sure what you mean by "linear difference". If you're asking for the advantages of the softmax function (compared to other functions), then maybe this is another question.
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Answers.Text: the square los, what has a derivative  expected - output. It is related to question, since it is about "What is the use of softmax function" if we have other one
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Answers.Text: @user8426627 I think that the usage of the softmax is due to the functionality it provides. For example, suppose you're trying to classify animals that are, in certain ways, very similar to each other: e.g. cheetahs and leopards. By having a probability distribution over the possible classes of a given input, you can understand if your model is certain or not regarding the class of the input. If your model tells you that there is a 40% probability that the input is a cheetah and 39% that it is a leopard, then you (and the model) will be a bit unsure regarding the actual class of the input.
Answers.UserDisplayName: nbro
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Answers.Body: <p>In short the softmax function helps with multi-classification i.e <em>output of more than one of two possibilities</em>.
  It works well this the categorical cross entropy.</p>
  
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Body: <p>What is the use of softmax function? Why was it used at the end of fully connected layer in convolution neural network?</p>
  
Tags: <neural-networks><convolutional-neural-networks>
Title: What is the use of softmax function in a CNN?
UserDisplayName: Abishekh Kc
ThreadText: # What is the use of softmax function in a CNN?
  
  (Asked by: username_0 on 2019-06-16)
  
  What is the use of softmax function? Why was it used at the end of fully connected layer in convolution neural network?
  
  **Tags:** neural-networks, convolutional-neural-networks
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2019-06-16. Score: 1
  
  The main purpose of the [softmax function](https://en.wikipedia.org/wiki/Softmax_function) is to transform the (unnormalised) output of $K$ units (which is e.g. represented as a vector of $K$ elements) of a fully-connected layer to a probability distribution (a normalised output), which is often represented as a vector of $K$ elements, each of which is between $0$ and $1$ (a probability) and the sum of all these elements is $1$ (a probability distribution).
  
  In the case of a classification task, the $i$th element of the vector produced by the softmax function corresponds to the probability of the input of the network of belonging to the $i$th class (e.g. a dog).
  
  > **Comments:**
  > **username_2**: but can it be replaced to argmax function (taking element with higher value as 1, everythin else is 0)  
  > **username_1**: @user8426627 You could do that, but you might lose the probabilistic interpretation of the results (classification). At the end, you will have to make a decision, so you will choose one (or more) of those outputs (anyway). The most obvious decision is to choose the class with the highest probability, but this might not always be the case.  
  > **username_2**: i just want to figure out, does softmax gains benefit when packpropagating from it compare to just use linear difference  
  > **username_1**: @user8426627 I'm not sure what you mean by "linear difference". If you're asking for the advantages of the softmax function (compared to other functions), then maybe this is another question.  
  > **username_2**: the square los, what has a derivative  expected - output. It is related to question, since it is about "What is the use of softmax function" if we have other one  
  > **username_1**: @user8426627 I think that the usage of the softmax is due to the functionality it provides. For example, suppose you're trying to classify animals that are, in certain ways, very similar to each other: e.g. cheetahs and leopards. By having a probability distribution over the possible classes of a given input, you can understand if your model is certain or not regarding the class of the input. If your model tells you that there is a 40% probability that the input is a cheetah and 39% that it is a leopard, then you (and the model) will be a bit unsure regarding the actual class of the input.  
  
  ---
  
  ### Answer by username_3 on 2019-06-16. Score: 1
  
  In short the softmax function helps with multi-classification i.e *output of more than one of two possibilities*.
  It works well this the categorical cross entropy.
  
  
  ---
  
  