adding README

README.md

+# KerasROOTClassification
+
+This is an attempt to simplify the training of Keras models from ROOT TTree input.
+
+The recommended usage is to put this module in your python path and
+create run scripts to define and train your model.
+
+For example:
+
+```python
+import numpy as np
+import logging
+
+from KerasROOTClassification import ClassificationProject
+
+logging.basicConfig()
+logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
+
+c = ClassificationProject("my_project", # this will also be the name of the project directory
+                          signal_trees = [(filename1, treename1)],
+                          bkg_trees = [(filename2, treename2),
+                                       (filename3, treename3),
+                          ],
+                          optimizer="Adam",
+                          selection="some-selection-expression",
+                          branches = ["var1", "var2", "var3"],
+                          weight_expr = "some-weight-expression",
+                          identifiers = ["var4", "var5"], # variables that identify which events were used for training
+                          step_bkg = 10, # take every 10th bkg event for training
+                          step_sig = 2, # take every second sig event for training
+)
+
+c.train(epochs=20)
+```
+
+Previously created projects can be inspected in iypthon like
+
+```
+ipython -i -m KerasROOTClassification.browse <project-dir>
+```
+
+# Conda setup
+
+An example for a mini conda setup that contains the nescessary packages:
+
+```sh
+conda install keras pandas matplotlib scikit-learn pydot graphviz jupyter
+pip install root_numpy
+```