def test_grad_s(self):

        "tests that the gradients with respect to s_i are 0 after doing a mean field update of s_i "

        model = self.model
        e_step = self.e_step
        X = self.X

        assert X.shape[0] == self.m

        model.test_batch_size = X.shape[0]

        init_H = e_step.init_H_hat(V = X)
        init_Mu1 = e_step.init_S_hat(V = X)

        prev_setting = config.compute_test_value
        config.compute_test_value= 'off'
        H, Mu1 = function([], outputs=[init_H, init_Mu1])()
        config.compute_test_value = prev_setting

        H = broadcast(H, self.m)
        Mu1 = broadcast(Mu1, self.m)

        H = np.cast[config.floatX](self.model.rng.uniform(0.,1.,H.shape))
        Mu1 = np.cast[config.floatX](self.model.rng.uniform(-5.,5.,Mu1.shape))



        H_var = T.matrix(name='H_var')
        H_var.tag.test_value = H
        Mu1_var = T.matrix(name='Mu1_var')
        Mu1_var.tag.test_value = Mu1
        idx = T.iscalar()
        idx.tag.test_value = 0


        S = e_step.infer_S_hat(V = X, H_hat = H_var, S_hat = Mu1_var)

        s_idx = S[:,idx]

        s_i_func = function([H_var,Mu1_var,idx],s_idx)

        sigma0 = model.alpha / 1.
        Sigma1 = e_step.infer_var_s1_hat()
        mu0 = T.zeros_like(model.mu)

        #by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
        # (they don't affect the outcome of this test and some of them are intractable )
        trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
                     model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0, var_s1_hat = Sigma1)

        grad_Mu1 = T.grad(trunc_kl.sum(), Mu1_var)

        grad_Mu1_idx = grad_Mu1[:,idx]

        grad_func = function([H_var, Mu1_var, idx], grad_Mu1_idx)

        for i in xrange(self.N):
            Mu1[:,i] = s_i_func(H, Mu1, i)

            g = grad_func(H,Mu1,i)

            assert not contains_nan(g)

            g_abs_max = np.abs(g).max()


            if g_abs_max > self.tol:
                raise Exception('after mean field step, gradient of kl divergence wrt mean field parameter should be 0, but here the max magnitude of a gradient element is '+str(g_abs_max)+' after updating s_'+str(i))
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/models/tests/test_s3c_inference.py/Test_S3C_Inference.test_grad_s/swapped operands of `/`