From 1a205b2d00be6484b713385efc1463b459af15cb Mon Sep 17 00:00:00 2001
From: "jan.middendorf@rwth-aachen.de" <jan.middendorf@rwth-aachen.de>
Date: Wed, 10 Jun 2020 14:38:54 +0200
Subject: [PATCH] Seed test
---
keras.py | 21 ++++++++++++++++++---
1 file changed, 18 insertions(+), 3 deletions(-)
diff --git a/keras.py b/keras.py
index 098f69f..69fc12f 100644
--- a/keras.py
+++ b/keras.py
@@ -67,7 +67,7 @@ class KFeed(object):
@property
def all(self):
- return set().union(*self.xyz)
+ return set().union(*self.xyw)
def balance(self, src):
return src
@@ -137,7 +137,7 @@ class KFeed(object):
return self.gensteps(*args, **kwargs)[0]
-def Normal(ref, indices=None, **kwargs):
+def Normal(ref, indices=None, name=None, **kwargs):
"""
Normalizing layer according to ref.
If given, only the variables corresponding to indices will be normalized.
@@ -145,14 +145,29 @@ def Normal(ref, indices=None, **kwargs):
mean = ref.mean(**kwargs)
std = ref.std(**kwargs)
if indices:
+ indices = np.array(indices)
replace = np.isin(np.arange(len(mean)), indices, invert=True)
mean[replace] = 0
std[replace] = 1
mul = 1.0 / std
add = -mean / std
- return tf.keras.layers.Lambda(lambda x: (x * mul) + add)
+ return tf.keras.layers.Lambda((lambda x: (x * mul) + add), name=name)
+def Onehot(index, n, name=None):
+ """
+ One hot encodes a variable referred to by index.
+ n is the number of different variables.
+ """
+ def to_onehot(x):
+ return tf.concat((
+ x[...,:index],
+ tf.gather(tf.eye(n), tf.cast(x[...,index], tf.int64)),
+ x[...,(index+1):],
+ ), axis=-1)
+ return tf.keras.layers.Lambda(to_onehot, name=name)
+
+
class Moment(tf.keras.metrics.Mean):
def __init__(self, order, label=False, **kwargs):
--
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