099e35f9db7e3098d8a88b99a9f5460b52bab2fc,tests/test_theano.py,,test_limb_darkened,#,60

Before Change

    // Compute the gradient using Theano
    varnames = sorted(theano_kwargs.keys())
    vars = [theano_kwargs[k] for k in varnames]
    computed = dict(zip(varnames, theano.function([], 
                                                  theano.grad(model[0], vars))()))

    // Compare
    for key in theano_kwargs.keys():
        if key == "zo":

After Change

    // Compute the gradient using Theano
    varnames = sorted(theano_kwargs.keys())
    vars = [theano_kwargs[k] for k in varnames]
    computed = {}
    for var in varnames:
        deriv = [theano.function([], 
                 theano.grad(model[i], theano_kwargs[var]))() 
                 for i in range(npts)]
        if var == "u":
            computed[var] = np.transpose(deriv)
        else:
            computed[var] = np.sum(deriv, axis=0)

    // Compare
    for key in theano_kwargs.keys():
        if key == "zo":
            continue
        assert np.allclose(np.squeeze(grad[key]), np.squeeze(computed[key]))

Instances