Softmax function

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  1. In this notebook I will explain the softmax function, its relationship with the negative log-likelihood, and its derivative when doing the backpropagation algorithm.[1]
  2. Its equation is simple, we just have to compute for the normalized exponential function of all the units in the layer.[1]
  3. In practice, the softmax function is used in tandem with the negative log-likelihood (NLL).[1]
  4. In this part, we will differentiate the softmax function with respect to the negative log-likelihood.[1]
  5. The softmax function is a function that turns a vector of K real values into a vector of K real values that sum to 1.[2]
  6. The softmax function is sometimes called the softargmax function, or multi-class logistic regression.[2]
  7. All the zi values are the elements of the input vector to the softmax function, and they can take any real value, positive, zero or negative.[2]
  8. If you use the softmax function in a machine learning model, you should be careful before interpreting it as a true probability, since it has a tendency to produce values very close to 0 or 1.[2]
  9. The picture below illustrates how a Softmax function looks like.[3]
  10. We just computed the top part of the Softmax function.[4]
  11. Any time we wish to represent a probability distribution over a discrete variable with n possible values, we may use the softmax function.[5]
  12. The softmax function takes as input a vector z of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers.[6]
  13. The softmax function is often used in the final layer of a neural network-based classifier.[6]
  14. In the field of reinforcement learning, a softmax function can be used to convert values into action probabilities.[6]
  15. Note that instead of making predictions from the input data x the softmax function takes as input the final hidden layer of the network h_{W,b}(x) .[7]
  16. In fact, we’re going to code an example model with Keras that makes use of the Softmax function for classification.[8]
  17. : If we are classifying images of handwritten digits (the MNIST data set), we want to force the classifier to choose only one identity for the digit by using the softmax function.[9]
  18. Here comes the softmax function into the picture.[10]
  19. Softmax function takes an N-dimensional vector of real numbers and transforms it into a vector of real number in range (0,1) which add upto 1.[11]
  20. As the name suggests, softmax function is a “soft” version of max function.[11]
  21. Due to the desirable property of softmax function outputting a probability distribution, we use it as the final layer in neural networks.[11]
  22. Cross Entropy Loss with Softmax function are used as the output layer extensively.[11]
  23. The Softmax function is used in many machine learning applications for multi-class classifications.[12]
  24. Notice that unlike the log-sigmoid and tanh activation functions, the softmax function needs all the pre-activation sums for a particular layer.[13]
  25. The softmax function, invented in 1959 by the social scientist R. Duncan Luce in the context of choice models, does precisely this.[14]
  26. We're starting to build up some feel for the softmax function and the way softmax layers behave.[15]

소스

  1. ↑ 1.0 1.1 1.2 1.3 Understanding softmax and the negative log-likelihood
  2. ↑ 2.0 2.1 2.2 2.3 Softmax Function
  3. ↑ The Softmax Function, Simplified
  4. ↑ Understand the Softmax Function in Minutes
  5. ↑ Softmax Activation Function with Python
  6. ↑ 6.0 6.1 6.2 Softmax function
  7. ↑ Unsupervised Feature Learning and Deep Learning Tutorial
  8. ↑ How does the Softmax activation function work? – MachineCurve
  9. ↑ Multi-label vs. Multi-class Classification: Sigmoid vs. Softmax
  10. ↑ Machine Learning with Python: Softmax as Activation Function
  11. ↑ 11.0 11.1 11.2 11.3 Classification and Loss Evaluation - Softmax and Cross Entropy Loss
  12. ↑ Sigmoid, Softmax and their derivatives
  13. ↑ Neural Network Activation Functions in C# -- Visual Studio Magazine
  14. ↑ 3.4. Softmax Regression — Dive into Deep Learning 0.15.1 documentation
  15. ↑ Neural networks and deep learning

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  • [{'LOWER': 'softmax'}, {'LEMMA': 'function'}]
  • [{'LOWER': 'normalized'}, {'LOWER': 'exponential'}, {'LEMMA': 'function'}]
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