thesffs/loss.py

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Python
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2021-12-30 09:23:31 +01:00
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import backend as K
def running_mean(x,n=100,K=K,tf=tf):
"""
Calculate the running mean of an array
"""
cumsum = tf.cumsum(x)
return (cumsum[n:] - cumsum[:-n]) / float(n)
def running_variance(x,n=100,K=K,tf=tf):
"""
Calculate the running variance of an array
"""
return running_mean(x**2,n=n,K=K,tf=tf)-running_mean(x,n=n,K=K,tf=tf)**2
def running_std(x,n=100,K=K,tf=tf):
"""
Calculate the running standard deviation of an array
"""
return K.sqrt(running_variance(x,n=n,K=K,tf=tf))
def loss2d(a,b,n=25,K=K,tf=tf):
q=b
x,y=q[:,0],q[:,1]
#sort by x, evaluate stds on y
dex=tf.argsort(x)
yy=tf.gather(y,dex)
ss=running_std(yy,n=n)
s=K.std(yy)
return K.mean(ss)/s
def numpyloss2d(a,b,n=25):
import numpy as np
q=np.concatenate((np.expand_dims(a,1),np.expand_dims(b,1)),axis=1)
np.gather=np.take
return loss2d(q,q,n=n,K=np,tf=np)
if __name__=='__main__':
import numpy as np
x=np.random.uniform(-1,1,size=(1000,2))
print(numpyloss2d(x[:,0],x[:,1],n=25))