Questions tagged [softmax]
For questions related to the softmax function, which a function that takes as input a vector of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers. The softmax is often used as the activation function of the output layer of a neural network.
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Which paper introduced the term “softmax”?
Nowadays, the softmax function is widely used in deep learning and, specifically, classification with neural networks. However, the origins of this term and function are almost never mentioned ...
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Isn't it true that using max over a softmax will be much slower because there is not a smooth gradient?
Isn't it true that using max over a softmax will be much slower because there is not a smooth gradient?
Max basically zeros out the gradients of all the non-maximum values. Especially at the beginning ...
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Transformers - is the self attention matrix softmax output (layer 1) symmetric?
Let's assume, that we embedded a vector of length 49 into a matrix using 512-d embeddings. If we then multiply the matrix by it transposed version we receive a matrix of 49 by 49. Which is symmetric. ...
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What do the authors of this paper mean by the bias term in this picture of a neural network implementation?
I am reading a paper implementing a deep deterministic policy gradient algorithm for portfolio management. My question is about a specific neural network implementation they depict in this picture (...
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Why does TensorFlow docs discourage using softmax as activation for the last layer?
The beginner colab example for tensorflow states:
Note: It is possible to bake this tf.nn.softmax in as the activation function for the last layer of the network....
