From feb1aa56b91aaa4abca498009d344a5df57b33fb Mon Sep 17 00:00:00 2001
From: lukas leufen <l.leufen@fz-juelich.de>
Date: Mon, 11 Nov 2019 15:41:23 +0100
Subject: [PATCH] first implementation of lrDecay, loss and lrCallback, no
tests yet
---
src/helpers.py | 48 ++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 48 insertions(+)
diff --git a/src/helpers.py b/src/helpers.py
index 424b2fb5..b4cbbcca 100644
--- a/src/helpers.py
+++ b/src/helpers.py
@@ -2,7 +2,55 @@ __author__ = 'Lukas Leufen'
__date__ = '2019-10-21'
+import logging
+import keras
+import keras.backend as K
+import math
+
+
def to_list(arg):
if not isinstance(arg, list):
arg = [arg]
return arg
+
+
+class Loss:
+
+ def l_p_loss(self, power):
+ def loss(y_true, y_pred):
+ return K.mean(K.pow(K.abs(y_pred - y_true), power), axis=-1)
+ return loss
+
+
+class lrDecay(keras.callbacks.History):
+
+ def __init__(self, base_lr: float = 0.01, drop: float = 0.96, epochs_drop: int = 8):
+ super(lrDecay, self).__init__()
+
+ self.lr = {'lr': []}
+ self.base_lr = base_lr
+ self.drop = drop
+ self.epochs_drop = epochs_drop
+
+ def on_epoch_begin(self, epoch: int, logs=None):
+ if epoch > 0:
+ current_lr = self.base_lr * math.pow(self.drop, math.floor(1 + epoch) / self.epochs_drop)
+ else:
+ current_lr = self.base_lr
+ K.set_value(self.model.optimizer.lr, current_lr)
+ self.lr['lr'].append(current_lr)
+ logging.info(f"Set learning rate to {current_lr}")
+ return K.get_value(self.model.optimizer.lr)
+
+
+class lrCallback(keras.callbacks.History):
+
+ def __init__(self):
+ super(lrCallback, self).__init__()
+ self.lr = None
+
+ def on_train_begin(self, logs=None):
+ self.lr = {}
+
+ def on_epoch_end(self, epoch, logs=None):
+ self.lr.append(self.model.optimizer.lr)
\ No newline at end of file
--
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