diff --git a/README.md b/README.md index 337b53d19bfda18e6531297783336644c8842d45..b128638fd038d0872ebad0cb51714fc6be676e7b 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -#How to run the python script# +# How to run the python script python TS2DEC.py -h @@ -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. -#Folder Structure# +# Folder Structure 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) - 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 -#Dependencies# +# Dependencies Keras ( >= 2.2.2) Scikit-learn ( >= 0.20.0)