def test_target_parameter():
srng = MRG_RandomStream()
pvals = np.array([[0.98, 0.01, 0.01], [0.01, 0.49, 0.50]])
def basic_target_parameter_test(x):
f = theano.function([], x)
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"))
basic_target_parameter_test(
srng.multinomial_wo_replacement(pvals=pvals.astype("float32"),
target="cpu"))
def test_undefined_grad():
srng = MRG_RandomStream(seed=1234)
# checking uniform distribution
low = tensor.scalar()
out = srng.uniform((), low=low)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, low)
high = tensor.scalar()
out = srng.uniform((), low=0, high=high)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, high)
out = srng.uniform((), low=low, high=high)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, (low, high))
# checking binomial distribution
prob = tensor.scalar()
out = srng.binomial((), p=prob)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, prob)
# checking multinomial distribution
prob1 = tensor.scalar()
prob2 = tensor.scalar()
p = [theano.tensor.as_tensor_variable([prob1, 0.5, 0.25])]
out = srng.multinomial(size=None, pvals=p, n=4)[0]
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(theano.tensor.sum(out), prob1)
p = [theano.tensor.as_tensor_variable([prob1, prob2])]
out = srng.multinomial(size=None, pvals=p, n=4)[0]
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(theano.tensor.sum(out), (prob1, prob2))
# checking choice
p = [theano.tensor.as_tensor_variable([prob1, prob2, 0.1, 0.2])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out[0], (prob1, prob2))
p = [theano.tensor.as_tensor_variable([prob1, prob2])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out[0], (prob1, prob2))
p = [theano.tensor.as_tensor_variable([prob1, 0.2, 0.3])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out[0], prob1)
# checking normal distribution
avg = tensor.scalar()
out = srng.normal((), avg=avg)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, avg)
std = tensor.scalar()
out = srng.normal((), avg=0, std=std)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, std)
out = srng.normal((), avg=avg, std=std)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, (avg, std))
# checking truncated normal distribution
avg = tensor.scalar()
out = srng.truncated_normal((), avg=avg)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, avg)
std = tensor.scalar()
out = srng.truncated_normal((), avg=0, std=std)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, std)
out = srng.truncated_normal((), avg=avg, std=std)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, (avg, std))
