Skip to content

P
pytensor
提交 1b750f2d authored 作者: mrTsjolder's avatar mrTsjolder
浏览文件
操作

Add tests for truncated normal

上级 f454ca19
隐藏空白字符变更
内嵌 并排
正在显示 包含 141 行增加 和 4 行删除
theano/sandbox/tests/test_rng_mrg.py
浏览文件 @ 1b750f2d
......@@ -298,7 +298,8 @@ def test_broadcastable():
pvals_2 = R.uniform(size=size2)
pvals_2 = pvals_2 / tensor.sum(pvals_2)
for distribution in [R.uniform, R.binomial, R.multinomial, R.multinomial_wo_replacement, R.normal]:
for distribution in [R.uniform, R.normal, R.truncated_normal,
R.binomial, R.multinomial, R.multinomial_wo_replacement]:
# multinomial or multinomial_wo_replacement does not support "size" argument,
# the sizes of them are implicitly defined with "pvals" argument.
if distribution in [R.multinomial, R.multinomial_wo_replacement]:
......@@ -378,7 +379,6 @@ def t_binomial(mean, size, const_size, var_input, input, steps, rtol):
@attr('slow')
def test_normal0():
steps = 50
std = 2.
if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
......@@ -391,7 +391,7 @@ def test_normal0():
sample_size_odd = (sample_size[0], sample_size[1] - 1)
x = tensor.matrix()
for size, const_size, var_input, input, avg, rtol, std_tol in [
test_cases = [
(sample_size, sample_size, [], [], -5., default_rtol, default_rtol),
(x.shape, sample_size, [x],
[np.zeros(sample_size, dtype=config.floatX)],
......@@ -409,8 +409,9 @@ def test_normal0():
# test with few samples at the same time
((1,), (1,), [], [], -5., default_rtol, 0.02),
((3,), (3,), [], [], -5., default_rtol, 0.02),
]:
]
for size, const_size, var_input, input, avg, rtol, std_tol in test_cases:
R = MRG_RandomStreams(234)
# Note: we specify `nstreams` to avoid a warning.
n = R.normal(size=size, avg=avg, std=std,
......@@ -438,6 +439,126 @@ def test_normal0():
prefix='numpy ', allow_01=True, inputs=input, mean_rtol=rtol)
@attr('slow')
def test_normal_truncation():
# just a copy of test_normal0 with extra bound check
steps = 50
std = 2.
# standard deviation is slightly less than for a regular Gaussian
# constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
target_std = .87962566103423978 * std
if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
config.mode == 'Mode' and config.linker in ['py']):
sample_size = (25, 30)
default_rtol = .02
else:
sample_size = (999, 50)
default_rtol = .01
sample_size_odd = (sample_size[0], sample_size[1] - 1)
x = tensor.matrix()
test_cases = [
(sample_size, sample_size, [], [], -5., default_rtol, default_rtol),
(x.shape, sample_size, [x],
[np.zeros(sample_size, dtype=config.floatX)],
-5., default_rtol, default_rtol),
# test odd value
(x.shape, sample_size_odd, [x],
[np.zeros(sample_size_odd, dtype=config.floatX)],
-5., default_rtol, default_rtol),
(sample_size, sample_size, [], [],
np.arange(np.prod(sample_size),
dtype='float32').reshape(sample_size),
10. * std / np.sqrt(steps), default_rtol),
# test empty size (scalar)
((), (), [], [], -5., default_rtol, 0.02),
# test with few samples at the same time
((1,), (1,), [], [], -5., default_rtol, 0.02),
((3,), (3,), [], [], -5., default_rtol, 0.02),
]
for size, const_size, var_input, input, avg, rtol, std_tol in test_cases:
R = MRG_RandomStreams(234)
# Note: we specify `nstreams` to avoid a warning.
n = R.normal(size=size, avg=avg, std=std, truncate=True,
nstreams=rng_mrg.guess_n_streams(size, warn=False))
f = theano.function(var_input, n)
# check if truncated at 2*std
samples = f(*input)
assert np.all(avg + 2 * std - samples >= 0), \
("bad upper bound? %s %s" % (samples, avg + 2 * std))
assert np.all(samples - (avg - 2 * std) >= 0), \
("bad lower bound? %s %s" % (samples, avg - 2 * std))
# Increase the number of steps if size implies only a few samples
if np.prod(const_size) < 10:
steps_ = steps * 50
else:
steps_ = steps
basictest(f, steps_, const_size, target_avg=avg, target_std=target_std,
prefix='mrg ', allow_01=True, inputs=input,
mean_rtol=rtol, std_tol=std_tol)
sys.stdout.flush()
@attr('slow')
def test_truncated_normal():
# just a copy of test_normal0 for truncated normal
steps = 50
std = 2.
if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
config.mode == 'Mode' and config.linker in ['py']):
sample_size = (25, 30)
default_rtol = .02
else:
sample_size = (999, 50)
default_rtol = .01
sample_size_odd = (sample_size[0], sample_size[1] - 1)
x = tensor.matrix()
test_cases = [
(sample_size, sample_size, [], [], -5., default_rtol, default_rtol),
(x.shape, sample_size, [x],
[np.zeros(sample_size, dtype=config.floatX)],
-5., default_rtol, default_rtol),
# test odd value
(x.shape, sample_size_odd, [x],
[np.zeros(sample_size_odd, dtype=config.floatX)],
-5., default_rtol, default_rtol),
(sample_size, sample_size, [], [],
np.arange(np.prod(sample_size),
dtype='float32').reshape(sample_size),
10. * std / np.sqrt(steps), default_rtol),
# test empty size (scalar)
((), (), [], [], -5., default_rtol, 0.02),
# test with few samples at the same time
((1,), (1,), [], [], -5., default_rtol, 0.02),
((3,), (3,), [], [], -5., default_rtol, 0.02),
]
for size, const_size, var_input, input, avg, rtol, std_tol in test_cases:
R = MRG_RandomStreams(234)
# Note: we specify `nstreams` to avoid a warning.
n = R.truncated_normal(size=size, avg=avg, std=std,
nstreams=rng_mrg.guess_n_streams(size, warn=False))
f = theano.function(var_input, n)
# Increase the number of steps if size implies only a few samples
if np.prod(const_size) < 10:
steps_ = steps * 60
else:
steps_ = steps
basictest(f, steps_, const_size, target_avg=avg, target_std=std,
prefix='mrg ', allow_01=True, inputs=input,
mean_rtol=rtol, std_tol=std_tol)
sys.stdout.flush()
def basic_multinomialtest(f, steps, sample_size, target_pvals, n_samples,
prefix="", mean_rtol=0.04):
......@@ -519,6 +640,7 @@ class T_MRG(unittest.TestCase):
self.assertRaises(ValueError, R.binomial, size)
self.assertRaises(ValueError, R.multinomial, size, 1, [])
self.assertRaises(ValueError, R.normal, size)
self.assertRaises(ValueError, R.truncated_normal, size)
def test_multiple_rng_aliasing():
......@@ -734,6 +856,19 @@ def test_undefined_grad():
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out,
(avg, std))
# checking truncated normal distribution
avg = tensor.scalar()
out = srng.truncated_normal((), avg=avg)
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out, avg)
std = tensor.scalar()
out = srng.truncated_normal((), avg=0, std=std)
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out, std)
out = srng.truncated_normal((), avg=avg, std=std)
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out,
(avg, std))
def test_f16_nonzero(mode=None, op_to_check=rng_mrg.mrg_uniform):
srng = MRG_RandomStreams(seed=utt.fetch_seed())
......@@ -754,6 +889,8 @@ def test_target_parameter():
assert isinstance(f(), np.ndarray)
basic_target_parameter_test(srng.uniform((3, 2), target='cpu'))
basic_target_parameter_test(srng.normal((3, 2), target='cpu'))
basic_target_parameter_test(srng.truncated_normal((3, 2), target='cpu'))
basic_target_parameter_test(srng.binomial((3, 2), target='cpu'))
basic_target_parameter_test(srng.multinomial(pvals=pvals.astype('float32'), target='cpu'))
basic_target_parameter_test(srng.choice(p=pvals.astype('float32'), replace=False, target='cpu'))
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论