def test_generic_compute_Lx(): ## now compare against theano version vv = T.matrix() gg = T.matrix() hh = T.matrix() aa = T.vector() bb = T.vector() cc = T.vector() xxw_mat = T.matrix() xxv_mat = T.matrix() xxw = T.vector() xxv = T.vector() xxa = T.vector() xxb = T.vector() xxc = T.vector() # test compute_Lx LLx = natural.generic_compute_Lx([vv, gg, hh], [xxw_mat, xxv_mat], [xxa, xxb, xxc]) f = theano.function([vv, gg, hh, xxw_mat, xxv_mat, xxa, xxb, xxc], LLx) t1 = time.time() rvals = f(v, g, h, xw_mat, xv_mat, xa, xb, xc) print 'Elapsed: ', time.time() - t1 numpy.testing.assert_almost_equal(Lx_w, rvals[0], decimal=3) numpy.testing.assert_almost_equal(Lx_v, rvals[1], decimal=3) numpy.testing.assert_almost_equal(Lx_a, rvals[2], decimal=3) numpy.testing.assert_almost_equal(Lx_b, rvals[3], decimal=3) numpy.testing.assert_almost_equal(Lx_c, rvals[4], decimal=3)
def test_generic_compute_Lx(): ## now compare against theano version vv = T.matrix() gg = T.matrix() hh = T.matrix() qq = T.matrix() aa = T.vector() bb = T.vector() cc = T.vector() dd = T.vector() xxw_mat = T.matrix() xxv_mat = T.matrix() xxz_mat = T.matrix() xxw = T.vector() xxv = T.vector() xxa = T.vector() xxb = T.vector() xxc = T.vector() xxd = T.vector() # test compute_Lx LLx = natural.generic_compute_Lx([vv, gg, hh, qq], [xxw_mat, xxv_mat, xxz_mat], [xxa, xxb, xxc, xxd]) f = theano.function( [vv, gg, hh, qq, xxw_mat, xxv_mat, xxz_mat, xxa, xxb, xxc, xxd], LLx) rvals = f(v, g, h, q, xw_mat, xv_mat, xz_mat, xa, xb, xc, xd) numpy.testing.assert_almost_equal(Lx_w, rvals[0], decimal=3) numpy.testing.assert_almost_equal(Lx_v, rvals[1], decimal=3) numpy.testing.assert_almost_equal(Lx_z, rvals[2], decimal=3) numpy.testing.assert_almost_equal(Lx_a, rvals[3], decimal=3) numpy.testing.assert_almost_equal(Lx_b, rvals[4], decimal=3) numpy.testing.assert_almost_equal(Lx_c, rvals[5], decimal=3) numpy.testing.assert_almost_equal(Lx_d, rvals[6], decimal=3)
def test_generic_compute_Lx(): ## now compare against theano version vv = T.matrix() gg = T.matrix() hh = T.matrix() qq = T.matrix() aa = T.vector() bb = T.vector() cc = T.vector() dd = T.vector() xxw_mat = T.matrix() xxv_mat = T.matrix() xxz_mat = T.matrix() xxw = T.vector() xxv = T.vector() xxa = T.vector() xxb = T.vector() xxc = T.vector() xxd = T.vector() # test compute_Lx LLx = natural.generic_compute_Lx([vv, gg, hh, qq], [xxw_mat, xxv_mat, xxz_mat], [xxa, xxb, xxc, xxd]) f = theano.function([vv, gg, hh, qq, xxw_mat, xxv_mat, xxz_mat, xxa, xxb, xxc, xxd], LLx) rvals = f(v, g, h, q, xw_mat, xv_mat, xz_mat, xa, xb, xc, xd) numpy.testing.assert_almost_equal(Lx_w, rvals[0], decimal=3) numpy.testing.assert_almost_equal(Lx_v, rvals[1], decimal=3) numpy.testing.assert_almost_equal(Lx_z, rvals[2], decimal=3) numpy.testing.assert_almost_equal(Lx_a, rvals[3], decimal=3) numpy.testing.assert_almost_equal(Lx_b, rvals[4], decimal=3) numpy.testing.assert_almost_equal(Lx_c, rvals[5], decimal=3) numpy.testing.assert_almost_equal(Lx_d, rvals[6], decimal=3)
def test_runtime(): ### theano implementation ### energies = - T.sum(T.dot(symb['v'], symb['W']) * symb['g'], axis=1) \ - T.sum(T.dot(symb['g'], symb['V']) * symb['h'], axis=1) \ - T.dot(symb['v'], symb['a']) \ - T.dot(symb['g'], symb['b']) \ - T.dot(symb['h'], symb['c']) # Fisher Implementation symb_params = [symb['W'], symb['V'], symb['a'], symb['b'], symb['c']] symb_x = [symb['x_W'], symb['x_V'], symb['x_a'], symb['x_b'], symb['x_c']] f_inputs = [symb['v'], symb['g'], symb['h']] + symb_params + symb_x fisher_Lx = fisher.compute_Lx(energies, symb_params, symb_x) fisher_func = theano.function(f_inputs, fisher_Lx) samples = [symb['v'], symb['g'], symb['h']] symb_weights = [symb['x_W'], symb['x_V']] symb_biases = [symb['x_a'], symb['x_b'], symb['x_c']] f_inputs = [symb['v'], symb['g'], symb['h']] + symb_weights + symb_biases natural_Lx = natural.generic_compute_Lx(samples, symb_weights, symb_biases) natural_func = theano.function(f_inputs, natural_Lx) t1 = time.time() fisher_rval = fisher_func(vals['v'], vals['g'], vals['h'], vals['W'], vals['V'], vals['a'], vals['b'], vals['c'], vals['x_W'], vals['x_V'], vals['x_a'], vals['x_b'], vals['x_c']) print 'Fisher runtime (s): ', time.time() - t1 t1 = time.time() nat_rval = natural_func(vals['v'], vals['g'], vals['h'], vals['x_W'], vals['x_V'], vals['x_a'], vals['x_b'], vals['x_c']) print 'Natural runtime (s): ', time.time() - t1 ### make sure the two return the same thing ### for (rval1, rval2) in zip(fisher_rval, nat_rval): numpy.testing.assert_almost_equal(rval1, rval2, decimal=2)