Update README.md

README.md

-#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)