pytorch tutorial
This commit is contained in:
Jakub Pokrywka
commit
e933463a32
100
iris.data
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100
iris.data
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5.9,3.0,4.2,1.5,Iris-versicolor
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5.9,3.2,4.8,1.8,Iris-versicolor
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||||
6.7,3.1,4.7,1.5,Iris-versicolor
|
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6.1,2.8,4.0,1.3,Iris-versicolor
|
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5.2,4.1,1.5,0.1,Iris-setosa
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5.4,3.4,1.7,0.2,Iris-setosa
|
||||
6.6,2.9,4.6,1.3,Iris-versicolor
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4.9,2.4,3.3,1.0,Iris-versicolor
|
||||
4.9,3.1,1.5,0.1,Iris-setosa
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5.0,3.3,1.4,0.2,Iris-setosa
|
||||
4.8,3.4,1.9,0.2,Iris-setosa
|
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5.6,2.9,3.6,1.3,Iris-versicolor
|
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6.7,3.1,4.4,1.4,Iris-versicolor
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5.1,3.8,1.5,0.3,Iris-setosa
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||||
5.7,3.0,4.2,1.2,Iris-versicolor
|
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5.8,2.7,4.1,1.0,Iris-versicolor
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6.1,2.8,4.7,1.2,Iris-versicolor
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4.4,2.9,1.4,0.2,Iris-setosa
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6.4,2.9,4.3,1.3,Iris-versicolor
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6.0,2.9,4.5,1.5,Iris-versicolor
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||||
5.2,3.4,1.4,0.2,Iris-setosa
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6.5,2.8,4.6,1.5,Iris-versicolor
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||||
6.8,2.8,4.8,1.4,Iris-versicolor
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||||
5.1,3.8,1.9,0.4,Iris-setosa
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||||
4.5,2.3,1.3,0.3,Iris-setosa
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||||
5.0,3.5,1.6,0.6,Iris-setosa
|
||||
5.4,3.9,1.3,0.4,Iris-setosa
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7.0,3.2,4.7,1.4,Iris-versicolor
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4.7,3.2,1.3,0.2,Iris-setosa
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5.6,3.0,4.5,1.5,Iris-versicolor
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5.5,2.5,4.0,1.3,Iris-versicolor
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5.7,3.8,1.7,0.3,Iris-setosa
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5.0,3.2,1.2,0.2,Iris-setosa
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6.7,3.0,5.0,1.7,Iris-versicolor
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5.2,2.7,3.9,1.4,Iris-versicolor
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5.5,2.6,4.4,1.2,Iris-versicolor
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5.4,3.7,1.5,0.2,Iris-setosa
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5.0,2.0,3.5,1.0,Iris-versicolor
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5.7,2.9,4.2,1.3,Iris-versicolor
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6.6,3.0,4.4,1.4,Iris-versicolor
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5.1,3.8,1.6,0.2,Iris-setosa
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5.7,2.8,4.1,1.3,Iris-versicolor
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5.8,2.6,4.0,1.2,Iris-versicolor
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4.9,3.1,1.5,0.1,Iris-setosa
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5.6,2.5,3.9,1.1,Iris-versicolor
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4.8,3.0,1.4,0.1,Iris-setosa
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5.1,3.3,1.7,0.5,Iris-setosa
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5.8,2.7,3.9,1.2,Iris-versicolor
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5.7,2.8,4.5,1.3,Iris-versicolor
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6.0,2.7,5.1,1.6,Iris-versicolor
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5.5,2.3,4.0,1.3,Iris-versicolor
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6.1,3.0,4.6,1.4,Iris-versicolor
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5.1,3.4,1.5,0.2,Iris-setosa
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5.4,3.4,1.5,0.4,Iris-setosa
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6.0,2.2,4.0,1.0,Iris-versicolor
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5.0,3.4,1.6,0.4,Iris-setosa
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6.3,3.3,4.7,1.6,Iris-versicolor
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5.7,2.6,3.5,1.0,Iris-versicolor
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4.6,3.2,1.4,0.2,Iris-setosa
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5.1,3.5,1.4,0.2,Iris-setosa
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6.4,3.2,4.5,1.5,Iris-versicolor
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5.0,3.5,1.3,0.3,Iris-setosa
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4.6,3.4,1.4,0.3,Iris-setosa
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4.9,3.0,1.4,0.2,Iris-setosa
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5.1,2.5,3.0,1.1,Iris-versicolor
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5.6,2.7,4.2,1.3,Iris-versicolor
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6.2,2.9,4.3,1.3,Iris-versicolor
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6.0,3.4,4.5,1.6,Iris-versicolor
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4.4,3.2,1.3,0.2,Iris-setosa
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5.2,3.5,1.5,0.2,Iris-setosa
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5.8,4.0,1.2,0.2,Iris-setosa
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5.0,3.6,1.4,0.2,Iris-setosa
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4.3,3.0,1.1,0.1,Iris-setosa
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5.7,4.4,1.5,0.4,Iris-setosa
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5.3,3.7,1.5,0.2,Iris-setosa
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4.8,3.1,1.6,0.2,Iris-setosa
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5.4,3.9,1.7,0.4,Iris-setosa
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5.6,3.0,4.1,1.3,Iris-versicolor
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4.8,3.4,1.6,0.2,Iris-setosa
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4.7,3.2,1.6,0.2,Iris-setosa
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5.0,3.4,1.5,0.2,Iris-setosa
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6.3,2.3,4.4,1.3,Iris-versicolor
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5.5,3.5,1.3,0.2,Iris-setosa
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6.2,2.2,4.5,1.5,Iris-versicolor
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5.1,3.5,1.4,0.3,Iris-setosa
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4.6,3.6,1.0,0.2,Iris-setosa
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6.1,2.9,4.7,1.4,Iris-versicolor
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4.4,3.0,1.3,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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5.0,2.3,3.3,1.0,Iris-versicolor
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6.3,2.5,4.9,1.5,Iris-versicolor
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5.5,4.2,1.4,0.2,Iris-setosa
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5.5,2.4,3.7,1.0,Iris-versicolor
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5.1,3.7,1.5,0.4,Iris-setosa
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5.0,3.0,1.6,0.2,Iris-setosa
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4.6,3.1,1.5,0.2,Iris-setosa
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4.8,3.0,1.4,0.3,Iris-setosa
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5.5,2.4,3.8,1.1,Iris-versicolor
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5.4,3.0,4.5,1.5,Iris-versicolor
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6.9,3.1,4.9,1.5,Iris-versicolor
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151
iris.data.multilabel
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151
iris.data.multilabel
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@ -0,0 +1,151 @@
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6.4,3.2,5.3,2.3,Iris-virginica
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5.1,3.8,1.5,0.3,Iris-setosa
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5.0,3.5,1.6,0.6,Iris-setosa
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5.7,3.0,4.2,1.2,Iris-versicolor
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5.7,2.5,5.0,2.0,Iris-virginica
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5.0,3.4,1.6,0.4,Iris-setosa
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5.4,3.4,1.5,0.4,Iris-setosa
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5.6,2.5,3.9,1.1,Iris-versicolor
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4.9,3.1,1.5,0.1,Iris-setosa
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4.9,2.4,3.3,1.0,Iris-versicolor
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5.9,3.2,4.8,1.8,Iris-versicolor
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6.7,3.1,5.6,2.4,Iris-virginica
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6.5,3.0,5.2,2.0,Iris-virginica
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5.5,2.3,4.0,1.3,Iris-versicolor
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6.7,3.3,5.7,2.1,Iris-virginica
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6.3,2.3,4.4,1.3,Iris-versicolor
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5.4,3.9,1.3,0.4,Iris-setosa
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6.4,3.2,4.5,1.5,Iris-versicolor
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4.9,3.1,1.5,0.1,Iris-setosa
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7.4,2.8,6.1,1.9,Iris-virginica
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4.8,3.4,1.6,0.2,Iris-setosa
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5.1,3.4,1.5,0.2,Iris-setosa
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6.0,2.2,4.0,1.0,Iris-versicolor
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6.5,3.0,5.5,1.8,Iris-virginica
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4.4,3.2,1.3,0.2,Iris-setosa
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5.0,3.2,1.2,0.2,Iris-setosa
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7.6,3.0,6.6,2.1,Iris-virginica
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5.0,2.3,3.3,1.0,Iris-versicolor
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5.7,2.9,4.2,1.3,Iris-versicolor
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5.1,3.3,1.7,0.5,Iris-setosa
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5.8,2.7,4.1,1.0,Iris-versicolor
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5.7,2.8,4.5,1.3,Iris-versicolor
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6.1,2.8,4.7,1.2,Iris-versicolor
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4.3,3.0,1.1,0.1,Iris-setosa
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5.5,2.4,3.8,1.1,Iris-versicolor
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5.8,2.7,5.1,1.9,Iris-virginica
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6.1,2.9,4.7,1.4,Iris-versicolor
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6.3,2.9,5.6,1.8,Iris-virginica
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6.1,3.0,4.9,1.8,Iris-virginica
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5.2,3.5,1.5,0.2,Iris-setosa
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4.6,3.1,1.5,0.2,Iris-setosa
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5.2,2.7,3.9,1.4,Iris-versicolor
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6.4,2.7,5.3,1.9,Iris-virginica
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6.3,2.5,4.9,1.5,Iris-versicolor
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5.5,4.2,1.4,0.2,Iris-setosa
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6.1,2.6,5.6,1.4,Iris-virginica
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4.8,3.0,1.4,0.3,Iris-setosa
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5.8,2.7,5.1,1.9,Iris-virginica
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4.9,3.1,1.5,0.1,Iris-setosa
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6.2,2.9,4.3,1.3,Iris-versicolor
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7.0,3.2,4.7,1.4,Iris-versicolor
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6.7,3.0,5.0,1.7,Iris-versicolor
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6.3,3.4,5.6,2.4,Iris-virginica
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5.6,3.0,4.5,1.5,Iris-versicolor
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5.9,3.0,5.1,1.8,Iris-virginica
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5.0,3.5,1.3,0.3,Iris-setosa
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7.2,3.0,5.8,1.6,Iris-virginica
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5.1,3.5,1.4,0.3,Iris-setosa
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5.1,3.8,1.9,0.4,Iris-setosa
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4.7,3.2,1.6,0.2,Iris-setosa
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7.2,3.6,6.1,2.5,Iris-virginica
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5.5,2.4,3.7,1.0,Iris-versicolor
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5.6,3.0,4.1,1.3,Iris-versicolor
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6.8,3.2,5.9,2.3,Iris-virginica
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5.4,3.7,1.5,0.2,Iris-setosa
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6.3,2.5,5.0,1.9,Iris-virginica
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4.6,3.4,1.4,0.3,Iris-setosa
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4.9,2.5,4.5,1.7,Iris-virginica
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5.0,3.3,1.4,0.2,Iris-setosa
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5.7,3.8,1.7,0.3,Iris-setosa
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4.4,2.9,1.4,0.2,Iris-setosa
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7.7,3.8,6.7,2.2,Iris-virginica
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6.5,3.0,5.8,2.2,Iris-virginica
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6.7,2.5,5.8,1.8,Iris-virginica
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7.3,2.9,6.3,1.8,Iris-virginica
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6.2,2.2,4.5,1.5,Iris-versicolor
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6.0,2.7,5.1,1.6,Iris-versicolor
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6.3,3.3,4.7,1.6,Iris-versicolor
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6.8,2.8,4.8,1.4,Iris-versicolor
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6.5,2.8,4.6,1.5,Iris-versicolor
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6.3,2.7,4.9,1.8,Iris-virginica
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6.6,2.9,4.6,1.3,Iris-versicolor
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6.9,3.1,5.1,2.3,Iris-virginica
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5.4,3.9,1.7,0.4,Iris-setosa
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5.7,4.4,1.5,0.4,Iris-setosa
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6.5,3.2,5.1,2.0,Iris-virginica
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6.9,3.2,5.7,2.3,Iris-virginica
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7.1,3.0,5.9,2.1,Iris-virginica
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5.8,4.0,1.2,0.2,Iris-setosa
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5.7,2.6,3.5,1.0,Iris-versicolor
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7.7,3.0,6.1,2.3,Iris-virginica
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6.3,3.3,6.0,2.5,Iris-virginica
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4.7,3.2,1.3,0.2,Iris-setosa
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5.6,2.9,3.6,1.3,Iris-versicolor
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6.0,3.4,4.5,1.6,Iris-versicolor
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5.6,2.7,4.2,1.3,Iris-versicolor
|
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4.4,3.0,1.3,0.2,Iris-setosa
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5.8,2.8,5.1,2.4,Iris-virginica
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6.7,3.1,4.7,1.5,Iris-versicolor
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6.6,3.0,4.4,1.4,Iris-versicolor
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4.8,3.1,1.6,0.2,Iris-setosa
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5.5,2.5,4.0,1.3,Iris-versicolor
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6.4,2.9,4.3,1.3,Iris-versicolor
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6.3,2.8,5.1,1.5,Iris-virginica
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5.1,2.5,3.0,1.1,Iris-versicolor
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6.4,2.8,5.6,2.1,Iris-virginica
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4.5,2.3,1.3,0.3,Iris-setosa
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6.4,2.8,5.6,2.2,Iris-virginica
|
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4.8,3.0,1.4,0.1,Iris-setosa
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7.9,3.8,6.4,2.0,Iris-virginica
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6.7,3.1,4.4,1.4,Iris-versicolor
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5.0,2.0,3.5,1.0,Iris-versicolor
|
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5.4,3.0,4.5,1.5,Iris-versicolor
|
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5.0,3.4,1.5,0.2,Iris-setosa
|
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5.2,3.4,1.4,0.2,Iris-setosa
|
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5.9,3.0,4.2,1.5,Iris-versicolor
|
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5.1,3.7,1.5,0.4,Iris-setosa
|
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4.9,3.0,1.4,0.2,Iris-setosa
|
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4.8,3.4,1.9,0.2,Iris-setosa
|
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6.2,2.8,4.8,1.8,Iris-virginica
|
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5.3,3.7,1.5,0.2,Iris-setosa
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5.5,2.6,4.4,1.2,Iris-versicolor
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6.2,3.4,5.4,2.3,Iris-virginica
|
||||
5.1,3.8,1.6,0.2,Iris-setosa
|
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5.8,2.7,3.9,1.2,Iris-versicolor
|
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6.9,3.1,4.9,1.5,Iris-versicolor
|
||||
6.1,2.8,4.0,1.3,Iris-versicolor
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6.7,3.3,5.7,2.5,Iris-virginica
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7.7,2.8,6.7,2.0,Iris-virginica
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6.1,3.0,4.6,1.4,Iris-versicolor
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5.0,3.0,1.6,0.2,Iris-setosa
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5.4,3.4,1.7,0.2,Iris-setosa
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6.8,3.0,5.5,2.1,Iris-virginica
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||||
5.7,2.8,4.1,1.3,Iris-versicolor
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5.8,2.6,4.0,1.2,Iris-versicolor
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5.6,2.8,4.9,2.0,Iris-virginica
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4.6,3.2,1.4,0.2,Iris-setosa
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6.4,3.1,5.5,1.8,Iris-virginica
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7.7,2.6,6.9,2.3,Iris-virginica
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5.1,3.5,1.4,0.2,Iris-setosa
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6.7,3.0,5.2,2.3,Iris-virginica
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5.0,3.6,1.4,0.2,Iris-setosa
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6.9,3.1,5.4,2.1,Iris-virginica
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4.6,3.6,1.0,0.2,Iris-setosa
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5.5,3.5,1.3,0.2,Iris-setosa
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5.2,4.1,1.5,0.1,Iris-setosa
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6.0,2.2,5.0,1.5,Iris-virginica
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6.0,3.0,4.8,1.8,Iris-virginica
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7.2,3.2,6.0,1.8,Iris-virginica
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6.0,2.9,4.5,1.5,Iris-versicolor
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69
iris.names
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69
iris.names
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1. Title: Iris Plants Database
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Updated Sept 21 by C.Blake - Added discrepency information
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2. Sources:
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(a) Creator: R.A. Fisher
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(b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
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(c) Date: July, 1988
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3. Past Usage:
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- Publications: too many to mention!!! Here are a few.
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1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
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Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
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to Mathematical Statistics" (John Wiley, NY, 1950).
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2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
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(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
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3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
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Structure and Classification Rule for Recognition in Partially Exposed
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Environments". IEEE Transactions on Pattern Analysis and Machine
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Intelligence, Vol. PAMI-2, No. 1, 67-71.
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-- Results:
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-- very low misclassification rates (0% for the setosa class)
|
||||
4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
|
||||
Transactions on Information Theory, May 1972, 431-433.
|
||||
-- Results:
|
||||
-- very low misclassification rates again
|
||||
5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
|
||||
conceptual clustering system finds 3 classes in the data.
|
||||
|
||||
4. Relevant Information:
|
||||
--- This is perhaps the best known database to be found in the pattern
|
||||
recognition literature. Fisher's paper is a classic in the field
|
||||
and is referenced frequently to this day. (See Duda & Hart, for
|
||||
example.) The data set contains 3 classes of 50 instances each,
|
||||
where each class refers to a type of iris plant. One class is
|
||||
linearly separable from the other 2; the latter are NOT linearly
|
||||
separable from each other.
|
||||
--- Predicted attribute: class of iris plant.
|
||||
--- This is an exceedingly simple domain.
|
||||
--- This data differs from the data presented in Fishers article
|
||||
(identified by Steve Chadwick, spchadwick@espeedaz.net )
|
||||
The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
|
||||
where the error is in the fourth feature.
|
||||
The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
|
||||
where the errors are in the second and third features.
|
||||
|
||||
5. Number of Instances: 150 (50 in each of three classes)
|
||||
|
||||
6. Number of Attributes: 4 numeric, predictive attributes and the class
|
||||
|
||||
7. Attribute Information:
|
||||
1. sepal length in cm
|
||||
2. sepal width in cm
|
||||
3. petal length in cm
|
||||
4. petal width in cm
|
||||
5. class:
|
||||
-- Iris Setosa
|
||||
-- Iris Versicolour
|
||||
-- Iris Virginica
|
||||
|
||||
8. Missing Attribute Values: None
|
||||
|
||||
Summary Statistics:
|
||||
Min Max Mean SD Class Correlation
|
||||
sepal length: 4.3 7.9 5.84 0.83 0.7826
|
||||
sepal width: 2.0 4.4 3.05 0.43 -0.4194
|
||||
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
|
||||
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
|
||||
|
||||
9. Class Distribution: 33.3% for each of 3 classes.
|
||||
121
mieszkania.tsv
Normal file
121
mieszkania.tsv
Normal file
@ -0,0 +1,121 @@
|
||||
powierzchnia cena
|
||||
53 215000
|
||||
60.01 219990
|
||||
54 285000
|
||||
60 330000
|
||||
63 212000
|
||||
39 219000
|
||||
76.11 399000
|
||||
48 119000
|
||||
42.19 260000
|
||||
53.41 323000
|
||||
65.65 555000
|
||||
65 185000
|
||||
55 247000
|
||||
100 280000
|
||||
56 224000
|
||||
39 230000
|
||||
42.3 179000
|
||||
49.65 305000
|
||||
68 345000
|
||||
37 145000
|
||||
103 529000
|
||||
62.3 209000
|
||||
17.65 42000
|
||||
45 500000
|
||||
36.15 140000
|
||||
45 159000
|
||||
50 130000
|
||||
48 84000
|
||||
36 359000
|
||||
39.3 116400
|
||||
49.48 136950
|
||||
26 85000
|
||||
72 469000
|
||||
64 239000
|
||||
55 435000
|
||||
90 175903
|
||||
90 175903
|
||||
90 175903
|
||||
127.88 1710000
|
||||
59 649000
|
||||
48.7 240000
|
||||
73 259000
|
||||
32.9 275000
|
||||
64 170000
|
||||
44.72 174408
|
||||
68 275000
|
||||
38 323000
|
||||
35 110000
|
||||
63 165000
|
||||
25 69000
|
||||
50 290000
|
||||
76.312 572325
|
||||
65 429000
|
||||
52.5 499000
|
||||
58 145000
|
||||
34 95000
|
||||
46 280000
|
||||
38 120000
|
||||
52 269000
|
||||
47 105000
|
||||
63 266000
|
||||
67.79 275000
|
||||
60 550000
|
||||
107 1230000
|
||||
53 228000
|
||||
48.65 148000
|
||||
39 140000
|
||||
23 170000
|
||||
35 195000
|
||||
71.19 245000
|
||||
75 329000
|
||||
53 185000
|
||||
51 135000
|
||||
42 133000
|
||||
38 142000
|
||||
45.6 470000
|
||||
50 194000
|
||||
29 158999
|
||||
28.8 199000
|
||||
36 199000
|
||||
57.43 385621
|
||||
57.71 402305
|
||||
60.12 395000
|
||||
38 210000
|
||||
56.28 419000
|
||||
60 346800
|
||||
41 295000
|
||||
28.7 219000
|
||||
39 275000
|
||||
37 105000
|
||||
47 330000
|
||||
64 435000
|
||||
96 151200
|
||||
35.34 87000
|
||||
101 489000
|
||||
50 129000
|
||||
49.5 315000
|
||||
14 2000
|
||||
31 110000
|
||||
50.9 265000
|
||||
117 129000
|
||||
52.2 250000
|
||||
28 140000
|
||||
15 5000
|
||||
41.7 249000
|
||||
56.4 490000
|
||||
30.9 161000
|
||||
42.3 229000
|
||||
53 270000
|
||||
72.4 409000
|
||||
52.9 370000
|
||||
37.77 135000
|
||||
82 260000
|
||||
32 195000
|
||||
59 590000
|
||||
62.01 205000
|
||||
52.5 543000
|
||||
56 170000
|
||||
67.61 285000
|
||||
51 494000
|
||||
|
22
pytorch1.py
Executable file
22
pytorch1.py
Executable file
@ -0,0 +1,22 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
def fun(x):
|
||||
return 2*x**4 - x**3 + 3.5*x + 10
|
||||
|
||||
|
||||
x = torch.tensor(5., requires_grad=True)
|
||||
|
||||
learning_rate = torch.tensor(0.01)
|
||||
|
||||
for _ in range(100):
|
||||
y = fun(x)
|
||||
print(x, " => ", y)
|
||||
y.backward()
|
||||
|
||||
with torch.no_grad():
|
||||
x = x - learning_rate * x.grad
|
||||
|
||||
x.requires_grad_(True)
|
||||
71
pytorch10.py
Executable file
71
pytorch10.py
Executable file
@ -0,0 +1,71 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import torch
|
||||
import pandas as pd
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
data = pd.read_csv('iris.data.multilabel', sep=',', header=None)
|
||||
NAMES_DICT = {
|
||||
'Iris-setosa': 0,
|
||||
'Iris-versicolor': 1,
|
||||
'Iris-virginica': 2}
|
||||
|
||||
data[5] = data[4].apply(lambda x: NAMES_DICT[x])
|
||||
|
||||
x = torch.tensor(data[[0,1,2,3]].values, dtype=torch.float)
|
||||
y = torch.tensor(data[5], dtype=torch.long)
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=42)
|
||||
|
||||
|
||||
class Network(torch.nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super(Network, self).__init__()
|
||||
self.fc1 = torch.nn.Linear(4, 4)
|
||||
self.fc2 = torch.nn.Linear(4, 3)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.fc2(x)
|
||||
x = torch.nn.functional.softmax(x)
|
||||
return x
|
||||
|
||||
|
||||
network = Network()
|
||||
optimizer = torch.optim.SGD(network.parameters(), lr=0.002)
|
||||
criterion = torch.nn.CrossEntropyLoss(reduction='sum')
|
||||
|
||||
samples_in_batch = 5
|
||||
|
||||
for epoch in range(3000):
|
||||
|
||||
network.train()
|
||||
for i in range(0, len(X_train), samples_in_batch):
|
||||
batch_x = X_train[i:i + samples_in_batch]
|
||||
batch_y = y_train[i:i + samples_in_batch]
|
||||
optimizer.zero_grad()
|
||||
ypredicted = network(batch_x)
|
||||
|
||||
loss = criterion(ypredicted, batch_y)
|
||||
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
network.eval()
|
||||
predicted_correct = 0
|
||||
loss_sum = 0
|
||||
for i in range(0, len(X_test), samples_in_batch):
|
||||
batch_x = X_test[i:i + samples_in_batch]
|
||||
batch_y = y_test[i:i + samples_in_batch]
|
||||
optimizer.zero_grad()
|
||||
ypredicted = network(batch_x)
|
||||
y_most_probable_class = torch.max(ypredicted,1)[1]
|
||||
|
||||
loss = criterion(ypredicted, batch_y)
|
||||
|
||||
predicted_correct += sum(y_most_probable_class == batch_y).item()
|
||||
|
||||
|
||||
accuracy = 100 * predicted_correct / len(y_test)
|
||||
print('{:.3}'.format(loss.item()), "\t => ", accuracy, '% accuracy')
|
||||
29
pytorch2.py
Executable file
29
pytorch2.py
Executable file
@ -0,0 +1,29 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import torch
|
||||
|
||||
m = torch.tensor([[2., 1.], [-1., 2.]])
|
||||
|
||||
|
||||
def fun(x):
|
||||
return m @ x
|
||||
|
||||
|
||||
def loss(y):
|
||||
return torch.sum((y - torch.tensor([3., 2.]))**2)
|
||||
|
||||
|
||||
x = torch.rand(2, requires_grad=True)
|
||||
|
||||
learning_rate = torch.tensor(0.01)
|
||||
|
||||
for _ in range(100):
|
||||
y = fun(x)
|
||||
cost = loss(y)
|
||||
print(x, " => ", y, " ", cost)
|
||||
cost.backward()
|
||||
|
||||
with torch.no_grad():
|
||||
x = x - learning_rate * x.grad
|
||||
|
||||
x.requires_grad_(True)
|
||||
27
pytorch3.py
Executable file
27
pytorch3.py
Executable file
@ -0,0 +1,27 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import torch
|
||||
import pandas
|
||||
|
||||
|
||||
data = pandas.read_csv('mieszkania.tsv', sep='\t')
|
||||
|
||||
x = torch.tensor(data['powierzchnia'], dtype=torch.float)
|
||||
y = torch.tensor(data['cena'], dtype=torch.float)
|
||||
|
||||
w = torch.rand(1, requires_grad=True)
|
||||
|
||||
learning_rate = torch.tensor(0.0000001)
|
||||
|
||||
for _ in range(100):
|
||||
ypredicted = w * x
|
||||
cost = torch.sum((ypredicted - y) ** 2)
|
||||
|
||||
print(w, " => ", cost)
|
||||
|
||||
cost.backward()
|
||||
|
||||
with torch.no_grad():
|
||||
w = w - learning_rate * w.grad
|
||||
|
||||
w.requires_grad_(True)
|
||||
29
pytorch4.py
Executable file
29
pytorch4.py
Executable file
@ -0,0 +1,29 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import torch
|
||||
import pandas
|
||||
|
||||
|
||||
data = pandas.read_csv('mieszkania.tsv', sep='\t')
|
||||
|
||||
x1 = torch.tensor(data['powierzchnia'], dtype=torch.float)
|
||||
x0 = torch.ones(x1.size(0))
|
||||
x = torch.stack((x0, x1)).transpose(0, 1)
|
||||
y = torch.tensor(data['cena'], dtype=torch.float)
|
||||
|
||||
w = torch.rand(2, requires_grad=True)
|
||||
|
||||
learning_rate = torch.tensor(0.000002)
|
||||
|
||||
for _ in range(400000):
|
||||
ypredicted = x @ w
|
||||
cost = torch.sum((ypredicted - y) ** 2)
|
||||
|
||||
print(w, " => ", cost)
|
||||
|
||||
cost.backward()
|
||||
|
||||
with torch.no_grad():
|
||||
w = w - learning_rate * w.grad
|
||||
|
||||
w.requires_grad_(True)
|
||||
32
pytorch5.py
Executable file
32
pytorch5.py
Executable file
@ -0,0 +1,32 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import torch
|
||||
import pandas as pd
|
||||
|
||||
|
||||
data = pd.read_csv('iris.data',sep = ',', header = None)
|
||||
data[5] = data[4].apply(lambda x: 1 if x == 'Iris-versicolor' else 0)
|
||||
|
||||
x1 = torch.tensor(data[0], dtype=torch.float)
|
||||
x0 = torch.ones(x1.size(0))
|
||||
x = torch.stack((x0, x1)).transpose(0, 1)
|
||||
y = torch.tensor(data[5], dtype=torch.float)
|
||||
|
||||
w = torch.rand(2, requires_grad=True)
|
||||
|
||||
learning_rate = torch.tensor(0.005)
|
||||
|
||||
for _ in range(3000):
|
||||
ypredicted = torch.nn.functional.sigmoid(x @ w)
|
||||
|
||||
# cost = torch.sum((ypredicted - y) ** 2)
|
||||
cost = - (torch.sum(y*torch.log(ypredicted) + (torch.ones_like(y) - y) * torch.log(1- ypredicted)))
|
||||
accuracy = 100 * sum((ypredicted > 0.5) == y).item() / len(ypredicted)
|
||||
print(w, " => ", cost, " => ", accuracy, '% accuracy')
|
||||
|
||||
cost.backward()
|
||||
|
||||
with torch.no_grad():
|
||||
w = w - learning_rate * w.grad
|
||||
|
||||
w.requires_grad_(True)
|
||||
29
pytorch6.py
Executable file
29
pytorch6.py
Executable file
@ -0,0 +1,29 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import torch
|
||||
import pandas as pd
|
||||
|
||||
|
||||
data = pd.read_csv('iris.data',sep = ',', header = None)
|
||||
data[5] = data[4].apply(lambda x: 1 if x == 'Iris-versicolor' else 0)
|
||||
|
||||
x1 = torch.tensor(data[0], dtype=torch.float)
|
||||
x0 = torch.ones(x1.size(0))
|
||||
x = torch.stack((x0, x1)).transpose(0, 1)
|
||||
y = torch.tensor(data[5], dtype=torch.float)
|
||||
|
||||
w = torch.rand(2, requires_grad=True)
|
||||
|
||||
optimizer = torch.optim.SGD([w], lr=0.005)
|
||||
|
||||
for _ in range(3000):
|
||||
optimizer.zero_grad()
|
||||
ypredicted = torch.nn.functional.sigmoid(x @ w)
|
||||
|
||||
cost = - (torch.sum(y*torch.log(ypredicted) + (torch.ones_like(y) - y) * torch.log(1- ypredicted)))
|
||||
accuracy = 100 * sum((ypredicted > 0.5) == y).item() / len(ypredicted)
|
||||
print(w, " => ", cost, " => ", accuracy, '% accuracy')
|
||||
|
||||
cost.backward()
|
||||
optimizer.step()
|
||||
|
||||
41
pytorch7.py
Executable file
41
pytorch7.py
Executable file
@ -0,0 +1,41 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import torch
|
||||
import pandas as pd
|
||||
|
||||
|
||||
data = pd.read_csv('iris.data',sep = ',', header = None)
|
||||
data[5] = data[4].apply(lambda x: 1 if x == 'Iris-versicolor' else 0)
|
||||
|
||||
x = torch.tensor(data[[0,1]].values, dtype=torch.float)
|
||||
y = torch.tensor(data[5], dtype=torch.float)
|
||||
|
||||
y = y.reshape(100,1)
|
||||
|
||||
class Network(torch.nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super(Network, self).__init__()
|
||||
self.fc = torch.nn.Linear(2,1)
|
||||
|
||||
def forward(self,x):
|
||||
x = self.fc(x)
|
||||
x = torch.nn.functional.sigmoid(x)
|
||||
|
||||
return x
|
||||
|
||||
network = Network()
|
||||
optimizer = torch.optim.SGD(network.parameters(), lr=0.002)
|
||||
criterion = torch.nn.BCELoss()
|
||||
|
||||
for _ in range(3000):
|
||||
optimizer.zero_grad()
|
||||
ypredicted = network(x)
|
||||
|
||||
loss = criterion(ypredicted,y)
|
||||
accuracy = 100 * sum((ypredicted > 0.5) == y).item() / len(ypredicted)
|
||||
print('{:.3}'.format(loss.item()), "\t => ", accuracy, '% accuracy')
|
||||
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
68
pytorch8.py
Executable file
68
pytorch8.py
Executable file
@ -0,0 +1,68 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import torch
|
||||
import pandas as pd
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
|
||||
data = pd.read_csv('iris.data',sep = ',', header = None)
|
||||
data[5] = data[4].apply(lambda x: 1 if x == 'Iris-versicolor' else 0)
|
||||
|
||||
|
||||
x = torch.tensor(data[[0,1,2,3]].values, dtype=torch.float)
|
||||
y = torch.tensor(data[5], dtype=torch.float)
|
||||
|
||||
y = y.reshape(100,1)
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(x,y, random_state = 42)
|
||||
|
||||
class Network(torch.nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super(Network, self).__init__()
|
||||
self.fc = torch.nn.Linear(4,1)
|
||||
|
||||
def forward(self,x):
|
||||
x = self.fc(x)
|
||||
x = torch.nn.functional.sigmoid(x)
|
||||
|
||||
return x
|
||||
|
||||
network = Network()
|
||||
optimizer = torch.optim.SGD(network.parameters(), lr=0.002)
|
||||
criterion = torch.nn.BCELoss()
|
||||
|
||||
samples_in_batch = 5
|
||||
|
||||
for epoch in range(3000):
|
||||
|
||||
network.train()
|
||||
for i in range(0,len(X_train),samples_in_batch):
|
||||
batch_x = X_train[i:i+samples_in_batch]
|
||||
batch_y = y_train[i:i+samples_in_batch]
|
||||
optimizer.zero_grad()
|
||||
ypredicted = network(batch_x)
|
||||
|
||||
loss = criterion(ypredicted,batch_y)
|
||||
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
|
||||
network.eval()
|
||||
predicted_correct = 0
|
||||
loss_sum = 0
|
||||
for i in range(0,len(X_test),samples_in_batch):
|
||||
batch_x = X_test[i:i+samples_in_batch]
|
||||
batch_y = y_test[i:i+samples_in_batch]
|
||||
optimizer.zero_grad()
|
||||
ypredicted = network(batch_x)
|
||||
|
||||
loss_sum += criterion(ypredicted,batch_y)
|
||||
|
||||
predicted_correct += sum(((ypredicted > 0.5) == batch_y)).item()
|
||||
|
||||
accuracy = 100 * predicted_correct / len(y_test)
|
||||
print('{:.3}'.format(loss.item()), "\t => ", accuracy, '% accuracy')
|
||||
|
||||
|
||||
69
pytorch9.py
Executable file
69
pytorch9.py
Executable file
@ -0,0 +1,69 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
import torch
|
||||
import pandas as pd
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
data = pd.read_csv('iris.data.multilabel', sep=',', header=None)
|
||||
NAMES_DICT = {
|
||||
'Iris-setosa': 0,
|
||||
'Iris-versicolor': 1,
|
||||
'Iris-virginica': 2}
|
||||
|
||||
data[5] = data[4].apply(lambda x: NAMES_DICT[x])
|
||||
|
||||
x = torch.tensor(data[[0,1,2,3]].values, dtype=torch.float)
|
||||
y = torch.tensor(data[5], dtype=torch.long)
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=42)
|
||||
|
||||
|
||||
class Network(torch.nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super(Network, self).__init__()
|
||||
self.fc = torch.nn.Linear(4, 3)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc(x)
|
||||
x = torch.nn.functional.softmax(x)
|
||||
return x
|
||||
|
||||
|
||||
network = Network()
|
||||
optimizer = torch.optim.SGD(network.parameters(), lr=0.002)
|
||||
criterion = torch.nn.CrossEntropyLoss(reduction='sum')
|
||||
|
||||
samples_in_batch = 5
|
||||
|
||||
for epoch in range(3000):
|
||||
|
||||
network.train()
|
||||
for i in range(0, len(X_train), samples_in_batch):
|
||||
batch_x = X_train[i:i + samples_in_batch]
|
||||
batch_y = y_train[i:i + samples_in_batch]
|
||||
optimizer.zero_grad()
|
||||
ypredicted = network(batch_x)
|
||||
|
||||
loss = criterion(ypredicted, batch_y)
|
||||
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
network.eval()
|
||||
predicted_correct = 0
|
||||
loss_sum = 0
|
||||
for i in range(0, len(X_test), samples_in_batch):
|
||||
batch_x = X_test[i:i + samples_in_batch]
|
||||
batch_y = y_test[i:i + samples_in_batch]
|
||||
optimizer.zero_grad()
|
||||
ypredicted = network(batch_x)
|
||||
y_most_probable_class = torch.max(ypredicted,1)[1]
|
||||
|
||||
loss = criterion(ypredicted, batch_y)
|
||||
|
||||
predicted_correct += sum(y_most_probable_class == batch_y).item()
|
||||
|
||||
|
||||
accuracy = 100 * predicted_correct / len(y_test)
|
||||
print('{:.3}'.format(loss.item()), "\t => ", accuracy, '% accuracy')
|
||||
Loading…
Reference in New Issue
Block a user