custom_metrics.py

import math
from keras import backend as K

# Define our custom metric
def PSNR(y_true, y_pred):
    max_pixel = 1.0
    return 10.0 * math.log10((max_pixel ** 2) / (K.mean(K.square(y_pred - y_true)))) 

# Define our custom loss function
def charbonnier(y_true, y_pred):
    epsilon = 1e-3
    error = y_true - y_pred
    p = K.sqrt(K.square(error) + K.square(epsilon))
    return K.mean(p)

# Compile our model
adam = Adam(lr=0.0001)
model.compile(loss=[charbonnier], metrics=[PSNR], optimizer=adam)