def test_apply_nets_regression():
'''Checks only the apply_nets without prob and classify
'''
pm = ParticleMesh(BoxSize=32.0, Nmesh=(8, 8, 8), dtype='f8')
engine = FastPMEngine(pm)
code = CodeSegment(engine)
#set up a network with 'nft' features, 'nl' hidden layers of size 'ls'
#and data set of size 'nd'
nft, nd = 5, 100
nl, lhs = 2, [20, 30]
ls = [nft] + lhs + [1]
if nl != len(lhs):
print('Number of layers is not the same as list of layer sizes')
wts, bias = [], []
nx, ny = nft, ls[0]
for i in range(nl + 1):
nx, ny = ls[i], ls[i + 1]
wt = numpy.random.uniform(size=nx * ny).reshape(nx, ny).copy()
wts.append(wt)
bt = numpy.random.uniform(size=ny).copy()
bias.append(bt)
#wts[-1] = wts[-1].reshape(-1)
bias[-1] = bias[-1].reshape(-1)
print(nft, nl, nd)
for i in range(nl + 1):
print(wts[i].shape, bias[i].shape)
acts = ['relu', 'relu']
arch = tuple(zip(wts, bias, acts))
features = numpy.random.uniform(size=nd * nft).reshape(nd, nft)
features -= features.mean(axis=0)
features /= features.std(axis=0)
print(features.shape)
#code.apply_nets(predict='predict', features='features', coeff=wts, \
# intercept=bias, Nd=nd, prob=False, classify=False)
code.apply_nets(predict='predict', features='features', arch=arch, Nd=nd)
eps = 1e-6
check_grad(code,
'predict',
'features',
init={'features': features},
eps=eps,
rtol=1e-8,
atol=1e-8)
def test_net_combination():
pm = ParticleMesh(BoxSize=8.0, Nmesh=(8, 8, 8), dtype='f8')
engine = FastPMEngine(pm)
code = CodeSegment(engine)
#set up a network with 'nft' features, 'nl' hidden layers of size 'ls'
#and data set of size 'nd'
nft, nd = 5, 100
nl, lhs = 2, [20, 30]
ls = [nft] + lhs + [1]
if nl != len(lhs):
print('Number of layers is not the same as list of layer sizes')
wts, bias = [], []
nx, ny = nft, ls[0]
for i in range(nl + 1):
nx, ny = ls[i], ls[i + 1]
wt = numpy.random.uniform(size=nx * ny).reshape(nx, ny).copy()
wts.append(wt)
bt = numpy.random.uniform(size=ny).copy()
bias.append(bt)
#wts[-1] = wts[-1].reshape(-1)
#bias[-1] = bias[-1].reshape(-1)
print(nft, nl, nd)
for i in range(nl + 1):
print(wts[i].shape, bias[i].shape)
features = numpy.random.uniform(size=nd * nft).reshape(nd, nft)
features -= features.mean(axis=0)
features /= features.std(axis=0)
code.apply_nets(predict='predict', features='features', coeff=wts, \
intercept=bias, Nd=nd, prob=True, classify=True)
eps = 1e-4
check_grad(code,
'predict',
'features',
init={'features': features},
eps=eps,
rtol=1e-8,
atol=1e-12)
