Commit ceb49e57 authored by Ienco Dino's avatar Ienco Dino
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#How to run the python script# # How to run the python script
python TS2DEC.py -h python TS2DEC.py -h
...@@ -15,7 +15,7 @@ The first file (optdigits/TS2DEC/2_10_3.npy) contains the clustering assignment ...@@ -15,7 +15,7 @@ The first file (optdigits/TS2DEC/2_10_3.npy) contains the clustering assignment
The second file (optdigits/TS2DEC/features_2_10_3.npy) contains the embedding representation generated by the encoder of TS2DEC. This file contains as many lines as the number of examples and 10 colmuns since the bottleneck layer has a dimensionality equal to 10. The second file (optdigits/TS2DEC/features_2_10_3.npy) contains the embedding representation generated by the encoder of TS2DEC. This file contains as many lines as the number of examples and 10 colmuns since the bottleneck layer has a dimensionality equal to 10.
#Folder Structure# # Folder Structure
For each benchmar (fMNIST, USPS, Reuters and Optdigits) we provide the data we have employed: For each benchmar (fMNIST, USPS, Reuters and Optdigits) we provide the data we have employed:
- data.npy contains the examples in a relational representation. For instance, consider the fMNIST dataset, data.npy is a numpy array of shape (70000, 784) - data.npy contains the examples in a relational representation. For instance, consider the fMNIST dataset, data.npy is a numpy array of shape (70000, 784)
- class.npy contains the class associated to each element of data.npy considering a positional notation. For instance, consider the fMNIST dataset, class.npy is a numpy array of shape (70000,) with 10 possible values (0-9). - class.npy contains the class associated to each element of data.npy considering a positional notation. For instance, consider the fMNIST dataset, class.npy is a numpy array of shape (70000,) with 10 possible values (0-9).
...@@ -25,7 +25,7 @@ For instance, considering the reuters dataset, in the folder constraints we have ...@@ -25,7 +25,7 @@ For instance, considering the reuters dataset, in the folder constraints we have
#Dependencies# # Dependencies
Keras ( >= 2.2.2) Keras ( >= 2.2.2)
Scikit-learn ( >= 0.20.0) Scikit-learn ( >= 0.20.0)
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