# Sparse Autoencoder Notation Summary

### From Ufldl

Here is a summary of the symbols used in our derivation of the sparse autoencoder:

Symbol | Meaning
--- | ---
| Input features for a training example, .
| Output/target values. Here, can be vector valued. In the case of an autoencoder, .
| The -th training example
| Output of our hypothesis on input , using parameters . This should be a vector of the same dimension as the target value .
| The parameter associated with the connection between unit in layer , and unit in layer .
| The bias term associated with unit in layer . Can also be thought of as the parameter associated with the connection between the bias unit in layer and unit in layer .
| Our parameter vector. It is useful to think of this as the result of taking the parameters and ``unrolling them into a long column vector.
| Activation (output) of unit in layer of the network. In addition, since layer is the input layer, we also have .
| The activation function. Throughout these notes, we used .
| Total weighted sum of inputs to unit in layer . Thus, .
| Learning rate parameter
| Number of units in layer (not counting the bias unit).
| Number layers in the network. Layer is usually the input layer, and layer the output layer.
| Weight decay parameter.
| For an autoencoder, its output; i.e., its reconstruction of the input . Same meaning as .
| Sparsity parameter, which specifies our desired level of sparsity
| The average activation of hidden unit (in the sparse autoencoder).
| Weight of the sparsity penalty term (in the sparse autoencoder objective).

Neural Networks | Backpropagation Algorithm | Gradient checking and advanced optimization | Autoencoders and Sparsity | Visualizing a Trained Autoencoder | Sparse Autoencoder Notation Summary | Exercise:Sparse Autoencoder

Language : 中文

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