def test_multi_convolutional_feature_map_twoplane_bprop():
size = (20, 20)
elems = np.prod(size)
fsize = (5, 5)
osize = (16, 16)
mfmap = MultiConvolutionalFeatureMap(fsize, size, 2)
mfmap.initialize()
inp = random.normal(size=size)
cnst = random.normal(size=size)
dout = np.ones(osize)
fprop = lambda y: mfmap.fprop((y.reshape(size), cnst)).sum()
approximate = fd_grad(fprop, inp.reshape(elems))
actual = mfmap.bprop(np.ones(osize), (inp, cnst))[0].reshape(elems)
assert_array_almost_equal(approximate, actual)
# Swap the order - should make no difference
fprop = lambda y: mfmap.fprop((cnst, y.reshape(size))).sum()
approximate = fd_grad(fprop, inp.reshape(elems))
actual = mfmap.bprop(np.ones(osize), (cnst, inp))[1].reshape(elems)
assert_array_almost_equal(approximate, actual)
def test_multi_convolutional_feature_map_twoplane_bprop():
size = (20, 20)
elems = np.prod(size)
fsize = (5, 5)
osize = (16, 16)
mfmap = MultiConvolutionalFeatureMap(fsize, size, 2)
mfmap.initialize()
inp = random.normal(size=size)
cnst = random.normal(size=size)
dout = np.ones(osize)
fprop = lambda y: mfmap.fprop((y.reshape(size), cnst)).sum()
approximate = fd_grad(fprop, inp.reshape(elems))
actual = mfmap.bprop(np.ones(osize), (inp, cnst))[0].reshape(elems)
assert_array_almost_equal(approximate, actual)
# Swap the order - should make no difference
fprop = lambda y: mfmap.fprop((cnst, y.reshape(size))).sum()
approximate = fd_grad(fprop, inp.reshape(elems))
actual = mfmap.bprop(np.ones(osize), (cnst, inp))[1].reshape(elems)
assert_array_almost_equal(approximate, actual)
def test_multi_convolutional_feature_map_singleplane_bprop():
size = (20, 20)
elems = np.prod(size)
fsize = (5, 5)
osize = (16, 16)
mfmap = MultiConvolutionalFeatureMap(fsize, size, 1)
mfmap.initialize()
in1 = random.normal(size=size)
dout = np.ones(osize)
bprop = lambda inp: mfmap.bprop(dout, inp)
grad1 = lambda var: bprop((var.reshape(size),))[0].reshape(elems)
func1 = lambda var: mfmap.fprop((var.reshape(size),)).sum()
varied_input = random.normal(size=size)
fd_grad1 = fd_grad(func1, varied_input.reshape(elems), 1e-4)
real_grad1 = grad1(varied_input)
assert_array_almost_equal(fd_grad1, real_grad1)
def test_multi_convolutional_feature_map_singleplane_bprop():
size = (20, 20)
elems = np.prod(size)
fsize = (5, 5)
osize = (16, 16)
mfmap = MultiConvolutionalFeatureMap(fsize, size, 1)
mfmap.initialize()
in1 = random.normal(size=size)
dout = np.ones(osize)
bprop = lambda inp: mfmap.bprop(dout, inp)
grad1 = lambda var: bprop((var.reshape(size), ))[0].reshape(elems)
func1 = lambda var: mfmap.fprop((var.reshape(size), )).sum()
varied_input = random.normal(size=size)
fd_grad1 = fd_grad(func1, varied_input.reshape(elems), 1e-4)
real_grad1 = grad1(varied_input)
assert_array_almost_equal(fd_grad1, real_grad1)