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)
