keras_test.py

import tensorflow as tf
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
import numpy as np
import matplotlib.pyplot as plt
import tensorflow.keras.backend as K

x = Input(shape=[5])
h = Dense(10)(x)
h = Dense(20)(h)
y = Dense(5)(h)

model = Model(x,y)

def create_loss(model):
    def deriv_loss(y_true, y_pred):
        l1 = tf.keras.losses.binary_crossentropy(y_true, y_pred)
        l2 = K.sum(K.abs(K.gradients(y_pred, model.input)))
        return l1

    return deriv_loss
 
model.compile(loss=create_loss(model))
ds = np.random.multivariate_normal(np.zeros(5), np.eye(5), size=[1000])
model.fit(ds, ds)