From 9994f0ad22c64d6214830f83dff6b061b56c6ed2 Mon Sep 17 00:00:00 2001
From: Nikolai <osterei33@gmx.de>
Date: Tue, 14 Aug 2018 09:37:17 +0200
Subject: [PATCH] starting rnn wrapper
---
toolkit.py | 42 ++++++++++++++++++++++++++++++++++++++++++
1 file changed, 42 insertions(+)
diff --git a/toolkit.py b/toolkit.py
index 37bb1b2..befd03e 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -1221,6 +1221,48 @@ class ClassificationProjectDataFrame(ClassificationProject):
pass
+class ClassificationProjectRNN(ClassificationProject):
+
+ """
+ A little wrapper to use recurrent units for things like jet collections
+ """
+
+ def __init__(self,
+ recurrent_branches=None,
+ mask_value=-999,
+ **kwargs):
+ self.recurrent_branches = recurrent_branches
+ if self.recurrent_branches is None:
+ self.recurrent_branches = []
+ self.mask_value = mask_value
+ super(ClassificationProjectRNN, self).__init__()
+
+
+ @property
+ def model():
+ pass
+
+
+ def yield_batch(self):
+ while True:
+ permutation = np.random.permutation
+ x_train, y_train, w_train = self.training_data
+ n_training = len(x_train)
+ for batch_start in range(0, n_training, self.batch_size):
+ pass
+ # # shuffle the entries for this class label
+ # rn_state = np.random.get_state()
+ # x_train[y_train==class_label] = np.random.permutation(x_train[y_train==class_label])
+ # np.random.set_state(rn_state)
+ # w_train[y_train==class_label] = np.random.permutation(w_train[y_train==class_label])
+ # # yield them batch wise
+ # for start in range(0, len(x_train[y_train==class_label]), int(self.batch_size/2)):
+ # yield (x_train[y_train==class_label][start:start+int(self.batch_size/2)],
+ # y_train[y_train==class_label][start:start+int(self.batch_size/2)],
+ # w_train[y_train==class_label][start:start+int(self.batch_size/2)]*self.balanced_class_weight[class_label])
+ # restart
+
+
if __name__ == "__main__":
logging.basicConfig()
--
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