def testLogistic(self, dtype):
key = random.PRNGKey(0)
rand = lambda key: random.logistic(key, (10000,), dtype)
crand = api.jit(rand)
uncompiled_samples = rand(key)
compiled_samples = crand(key)
for samples in [uncompiled_samples, compiled_samples]:
self._CheckKolmogorovSmirnovCDF(samples, scipy.stats.logistic().cdf)
def conditional_params_to_sample(rng, conditional_params):
means, inv_scales, logit_probs = conditional_params
rng_mix, rng_logistic = random.split(rng)
# Add channel dimension to one-hot mixture indicator
mix_indicator = _categorical_onehot(rng_mix, logit_probs)[..., jnp.newaxis]
# Use the mixture indicator to select the mean and inverse scale
mean = jnp.sum(means * mix_indicator, -4)
inv_scale = jnp.sum(inv_scales * mix_indicator, -4)
sample = mean + random.logistic(rng_logistic, mean.shape) / inv_scale
return snap_to_grid(sample)
def conditional_params_to_sample(rng, conditional_params):
means, inv_scales, logit_probs = conditional_params
_, h, w, c = means.shape
rng_mix, rng_logistic = random.split(rng)
mix_idx = np.broadcast_to(
_gumbel_max(rng_mix, logit_probs)[..., np.newaxis],
(h, w, c))[np.newaxis]
means = np.take_along_axis(means, mix_idx, 0)[0]
inv_scales = np.take_along_axis(inv_scales, mix_idx, 0)[0]
return (
means +
random.logistic(rng_logistic, means.shape, means.dtype) / inv_scales)
def testLogistic(self, dtype):
if jtu.device_under_test() == "tpu" and jnp.dtype(dtype).itemsize < 3:
raise SkipTest("random.logistic() not supported on TPU for 16-bit types.")
key = random.PRNGKey(0)
rand = lambda key: random.logistic(key, (10000,), dtype)
crand = api.jit(rand)
uncompiled_samples = rand(key)
compiled_samples = crand(key)
for samples in [uncompiled_samples, compiled_samples]:
self._CheckKolmogorovSmirnovCDF(samples, scipy.stats.logistic().cdf)
def sample(p, temperature, key, num_samples=1):
"""
Generate Binomial Concrete samples
:param p: Binomial Concrete params (interpreted as Bernoulli probabilities) (jax.numpy array)
:param temperature: temperature parameter
:param key: PRNG key
:param num_samples: number of samples
"""
tol = 1e-7
p = np.clip(p, tol, 1 - tol)
logit_p = logit(p)
base_randomness = random.logistic(key, shape=(num_samples, *p.shape))
return nn.sigmoid((logit_p + base_randomness) / (temperature + tol))
def logistic_mix_sample(nn_out, rng):
m, t, inv_scales, logit_weights = logistic_preprocess(nn_out)
rng_mix, rng_logistic = random.split(rng)
mix_idx = random.categorical(rng_mix, logit_weights, -3)
def select_mix(arr):
return jnp.squeeze(
jnp.take_along_axis(arr, jnp.expand_dims(mix_idx, (-4, -1)), -4),
-4)
m, t, inv_scales = map(lambda x: jnp.moveaxis(select_mix(x), -1, 0),
(m, t, inv_scales))
l = random.logistic(rng_logistic, m.shape) / inv_scales
img_red = m[0] + l[0]
img_green = m[1] + t[0] * img_red + l[1]
img_blue = m[2] + t[1] * img_red + t[2] * img_green + l[2]
return jnp.stack([img_red, img_green, img_blue], -1)
def logistic(loc=0.0, scale=1.0, size=None):
assert loc == 0.
assert scale == 1.
return JaxArray(jr.logistic(DEFAULT.split_key(), shape=_size2shape(size)))
def sample(self, key, sample_shape=()):
z = random.logistic(key,
shape=sample_shape + self.batch_shape +
self.event_shape)
return self.loc + z * self.scale
