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author | Timothy Hunter <timhunter@databricks.com> | 2015-12-08 18:40:21 -0800 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-12-08 18:40:21 -0800 |
commit | 765c67f5f2e0b1367e37883f662d313661e3a0d9 (patch) | |
tree | d8f03ee26be1dd0e6d427a9f0edab31447fb9856 /docs/ml-ann.md | |
parent | 39594894232e0b70c5ca8b0df137da0d61223fd5 (diff) | |
download | spark-765c67f5f2e0b1367e37883f662d313661e3a0d9.tar.gz spark-765c67f5f2e0b1367e37883f662d313661e3a0d9.tar.bz2 spark-765c67f5f2e0b1367e37883f662d313661e3a0d9.zip |
[SPARK-8517][ML][DOC] Reorganizes the spark.ml user guide
This PR moves pieces of the spark.ml user guide to reflect suggestions in SPARK-8517. It does not introduce new content, as requested.
<img width="192" alt="screen shot 2015-12-08 at 11 36 00 am" src="https://cloud.githubusercontent.com/assets/7594753/11666166/e82b84f2-9d9f-11e5-8904-e215424d8444.png">
Author: Timothy Hunter <timhunter@databricks.com>
Closes #10207 from thunterdb/spark-8517.
Diffstat (limited to 'docs/ml-ann.md')
-rw-r--r-- | docs/ml-ann.md | 62 |
1 files changed, 0 insertions, 62 deletions
diff --git a/docs/ml-ann.md b/docs/ml-ann.md deleted file mode 100644 index 6e763e8f41..0000000000 --- a/docs/ml-ann.md +++ /dev/null @@ -1,62 +0,0 @@ ---- -layout: global -title: Multilayer perceptron classifier - ML -displayTitle: <a href="ml-guide.html">ML</a> - Multilayer perceptron classifier ---- - - -`\[ -\newcommand{\R}{\mathbb{R}} -\newcommand{\E}{\mathbb{E}} -\newcommand{\x}{\mathbf{x}} -\newcommand{\y}{\mathbf{y}} -\newcommand{\wv}{\mathbf{w}} -\newcommand{\av}{\mathbf{\alpha}} -\newcommand{\bv}{\mathbf{b}} -\newcommand{\N}{\mathbb{N}} -\newcommand{\id}{\mathbf{I}} -\newcommand{\ind}{\mathbf{1}} -\newcommand{\0}{\mathbf{0}} -\newcommand{\unit}{\mathbf{e}} -\newcommand{\one}{\mathbf{1}} -\newcommand{\zero}{\mathbf{0}} -\]` - - -Multilayer perceptron classifier (MLPC) is a classifier based on the [feedforward artificial neural network](https://en.wikipedia.org/wiki/Feedforward_neural_network). -MLPC consists of multiple layers of nodes. -Each layer is fully connected to the next layer in the network. Nodes in the input layer represent the input data. All other nodes maps inputs to the outputs -by performing linear combination of the inputs with the node's weights `$\wv$` and bias `$\bv$` and applying an activation function. -It can be written in matrix form for MLPC with `$K+1$` layers as follows: -`\[ -\mathrm{y}(\x) = \mathrm{f_K}(...\mathrm{f_2}(\wv_2^T\mathrm{f_1}(\wv_1^T \x+b_1)+b_2)...+b_K) -\]` -Nodes in intermediate layers use sigmoid (logistic) function: -`\[ -\mathrm{f}(z_i) = \frac{1}{1 + e^{-z_i}} -\]` -Nodes in the output layer use softmax function: -`\[ -\mathrm{f}(z_i) = \frac{e^{z_i}}{\sum_{k=1}^N e^{z_k}} -\]` -The number of nodes `$N$` in the output layer corresponds to the number of classes. - -MLPC employes backpropagation for learning the model. We use logistic loss function for optimization and L-BFGS as optimization routine. - -**Examples** - -<div class="codetabs"> - -<div data-lang="scala" markdown="1"> -{% include_example scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala %} -</div> - -<div data-lang="java" markdown="1"> -{% include_example java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java %} -</div> - -<div data-lang="python" markdown="1"> -{% include_example python/ml/multilayer_perceptron_classification.py %} -</div> - -</div> |