Yexi Jiang created HAMA-770:
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Summary: Use a unified model to represent linear regression,
logistic regression, MLP, autoencoder, and deepNets
Key: HAMA-770
URL: https://issues.apache.org/jira/browse/HAMA-770
Project: Hama
Issue Type: Improvement
Reporter: Yexi Jiang
In principle, linear regression, logistic regression, MLP, autoencoder, and
deepNets can be represented by a generic neural network model. Using a generic
model and making the concrete models derive it can increase the reusability of
the code.
More concretely:
Linear regression is a two level neural network (one input layer and one output
layer) by setting the squashing function as identity function f( x ) = x, and
cost function as squared error.
Logistic regression is similar to linear regression, except that the squashing
function is set as sigmoid and cost function is set as cross entropy.
MLP is a neural nets with at least 2 layers of neurons. The squashing function
can be sigmoid, tanh (may be more) and cost function can be cross entropy,
squared error (may be more).
(sparse) autoencoder can be used for dimensional reduction (nonlinear) and
anomaly detection. Also, it can be used as the building block of deep nets.
Generally it is a three layer neural networks, where the size of input layer is
the same as output layer, and the size of hidden layer is typically less than
that of the input/output layer. Its cost function is squared error + KL
divergence.
deepNets is used for deep learning, a simple architecture is to stack several
autoencoder together.
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