test_mod_derivative.py

import numpy as np, theano.tensor as TT, theano

x = TT.scalar('x')
y = TT.scalar('y')

z = TT.mod(x**2, y)

# z = x**2+y**2
f = theano.function([x,y], z, allow_input_downcast=True)
dfdx = theano.function([x,y], TT.grad(z,x),allow_input_downcast=True) 
dfdy = theano.function([x,y], TT.grad(z,y),allow_input_downcast=True) 

import numdifftools as ndt


for i in xrange(100):
    print i
    xtest = np.random.randn()
    ytest = np.random.rand()
    assert np.allclose( ndt.Derivative(lambda _x : f(_x,ytest),step_nom=1e-8)(xtest), dfdx(xtest,ytest),atol=1e-8)
    assert np.allclose( ndt.Derivative(lambda _y : f(xtest, _y),step_nom=1e-8)(ytest), dfdy(xtest,ytest),atol=1e-8)