def test_multi_convolutional_feature_map_fprop():
cplane1 = ConvolutionalPlane((5, 5), (20, 20), bias=False)
cplane2 = ConvolutionalPlane((5, 5), (20, 20), bias=False)
sigmoid = TanhSigmoid((16, 16), bias=True)
mfmap = MultiConvolutionalFeatureMap((5, 5), (20, 20), 2)
mfmap.initialize()
cplane1.params[:] = mfmap.planes[0].params
cplane2.params[:] = mfmap.planes[1].params
sigmoid.params[:] = mfmap.params[0:1]
inputs1 = random.normal(size=(20, 20))
inputs2 = random.normal(size=(20, 20))
control = sigmoid.fprop(cplane1.fprop(inputs1) + cplane2.fprop(inputs2))
mfmap_out = mfmap.fprop([inputs1, inputs2])
assert_array_almost_equal(control, mfmap_out)
def test_multi_convolutional_feature_map_fprop():
cplane1 = ConvolutionalPlane((5, 5), (20, 20), bias=False)
cplane2 = ConvolutionalPlane((5, 5), (20, 20), bias=False)
sigmoid = TanhSigmoid((16, 16), bias=True)
mfmap = MultiConvolutionalFeatureMap((5, 5), (20, 20), 2)
mfmap.initialize()
cplane1.params[:] = mfmap.planes[0].params
cplane2.params[:] = mfmap.planes[1].params
sigmoid.params[:] = mfmap.params[0:1]
inputs1 = random.normal(size=(20, 20))
inputs2 = random.normal(size=(20, 20))
control = sigmoid.fprop(cplane1.fprop(inputs1) + cplane2.fprop(inputs2))
mfmap_out = mfmap.fprop([inputs1, inputs2])
assert_array_almost_equal(control, mfmap_out)
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)
def test_multi_convolutional_feature_map_twoplane_params():
size = (20, 20)
elems = np.prod(size)
fsize = (5, 5)
osize = (16, 16)
mfmap = MultiConvolutionalFeatureMap(fsize, size, 2)
mfmap.initialize()
inp1 = random.normal(size=size)
inp2 = random.normal(size=size)
dout = np.ones(osize)
def fprop_params1(params):
mfmap.planes[0].params[:] = params
return mfmap.fprop((inp1, inp2)).sum()
def fprop_params2(params):
mfmap.planes[1].params[:] = params
return mfmap.fprop((inp1, inp2)).sum()
def fprop_bias_adjust(params):
mfmap.params[0:1] = params
return mfmap.fprop((inp1, inp2)).sum()
# Reset the parameters after we calculate each gradient approximation.
resetparams = mfmap.params.copy()
params1 = mfmap.planes[0].params
params2 = mfmap.planes[1].params
paramsb = mfmap.params[0:1]
real = mfmap.grad(dout, (inp1, inp2))
approxb = fd_grad(fprop_bias_adjust, paramsb.copy(), 1e-4)
mfmap.params[:] = resetparams
approx1 = fd_grad(fprop_params1, params1.copy(), 1e-4)
mfmap.params[:] = resetparams
approx2 = fd_grad(fprop_params2, params2.copy(), 1e-4)
all_approx = np.concatenate((approxb, approx1, approx2))
assert_array_almost_equal(real, all_approx)
def test_multi_convolutional_feature_map_twoplane_params():
size = (20, 20)
elems = np.prod(size)
fsize = (5, 5)
osize = (16, 16)
mfmap = MultiConvolutionalFeatureMap(fsize, size, 2)
mfmap.initialize()
inp1 = random.normal(size=size)
inp2 = random.normal(size=size)
dout = np.ones(osize)
def fprop_params1(params):
mfmap.planes[0].params[:] = params
return mfmap.fprop((inp1, inp2)).sum()
def fprop_params2(params):
mfmap.planes[1].params[:] = params
return mfmap.fprop((inp1, inp2)).sum()
def fprop_bias_adjust(params):
mfmap.params[0:1] = params
return mfmap.fprop((inp1, inp2)).sum()
# Reset the parameters after we calculate each gradient approximation.
resetparams = mfmap.params.copy()
params1 = mfmap.planes[0].params
params2 = mfmap.planes[1].params
paramsb = mfmap.params[0:1]
real = mfmap.grad(dout, (inp1, inp2))
approxb = fd_grad(fprop_bias_adjust, paramsb.copy(), 1e-4)
mfmap.params[:] = resetparams
approx1 = fd_grad(fprop_params1, params1.copy(), 1e-4)
mfmap.params[:] = resetparams
approx2 = fd_grad(fprop_params2, params2.copy(), 1e-4)
all_approx = np.concatenate((approxb, approx1, approx2))
assert_array_almost_equal(real, all_approx)
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)