assert m.z.b == 6
assert all(m.z.c == [7, 8])
class MyModule(Module):
def __init__(self):
Module.__init__(self)
self.a = T.dscalar()
self.b = T.dscalar()
def _instance_initialize(self, obj):
obj.a = 4.5
obj.b = 3.5
def _instance_something(self, obj, a):
return obj.a + a
def test_pickle_module():
M = MyModule()
m = M.make()
mm = copy.deepcopy(m)
assert m.a == mm.a
assert m.b == mm.b
if __name__ == '__main__':
from theano.tests import main
# main(__file__[:-3])
main("test_module")
# t=T_test_module()
# t.test_shared_members()
# tests = unittest.TestLoader().loadTestsFromModule("T_test_module")
# tests.debug()
# normal with explicit size and size implicit in parameters
# cf. NOTE 1
post_r, out = normal(rng_R, avg=avg, std=std, size=size_val)
self._compile_and_check([rng_R, avg, std], [out], [rng_R_val, avg_val,
std_val], RandomFunction)
# multinomial with tensor-3 probabilities
"""
#multinomial infer_shape don't work.
pvals = dtensor3()
n = iscalar()
post_r, out = multinomial(rng_R, n=n, pvals=pvals, size=(1, -1))
pvals_val = [[[.1, .9], [.2, .8], [.3, .7]]]
n_val = 9
self._compile_and_check([rng_R, n, pvals], [out],
[rng_R_val, n_val,
pvals_val], RandomFunction)
post_r, out = multinomial(rng_R, n=n, pvals=pvals, size=(10, 1, -1))
self._compile_and_check([rng_R, n, pvals], [out],
[rng_R_val, n_val,
pvals_val], RandomFunction)
"""
if __name__ == '__main__':
from theano.tests import main
main("test_raw_random")
# Specifying the size explicitly
numpy_rng = numpy.random.RandomState(int(seed_gen.randint(2**30)))
val2 = made.g(n_val, pvals_val)
numpy_val2 = numpy.asarray([numpy_rng.multinomial(n=nv, pvals=pv)
for nv, pv in zip(n_val, pvals_val)])
assert numpy.all(val2 == numpy_val2)
self.assertRaises(ValueError, made.g, n_val[:-1], pvals_val[:-1])
def test_dtype(self):
m = Module()
m.random = RandomStreams(utt.fetch_seed())
low = tensor.lscalar()
high = tensor.lscalar()
out = m.random.random_integers(low=low, high=high, size=(20,), dtype='int8')
assert out.dtype == 'int8'
m.f = Method([low, high], out)
made = m.make()
made.random.initialize()
val0 = made.f(0, 9)
assert val0.dtype == 'int8'
val1 = made.f(255, 257)
assert val1.dtype == 'int8'
assert numpy.all(abs(val1) <= 1)
if __name__ == '__main__':
from theano.tests import main
main("test_randomstreams")
meant to detect a previous bug where state_updates was initialized as a
class-attribute, instead of the __init__ function.
"""
rng1 = RandomStreams(1234)
rng2 = RandomStreams(2392)
assert rng1.state_updates is not rng2.state_updates
assert rng1.gen_seedgen is not rng2.gen_seedgen
def test_random_state_transfer(self):
"""
Test that random state can be transferred from one theano graph to another.
"""
class Graph:
def __init__(self, seed=123):
self.rng = RandomStreams(seed)
self.y = self.rng.uniform(size=(1,))
g1 = Graph(seed=123)
f1 = function([], g1.y)
g2 = Graph(seed=987)
f2 = function([], g2.y)
for (su1, su2) in zip(g1.rng.state_updates, g2.rng.state_updates):
su2[0].set_value(su1[0].get_value())
numpy.testing.assert_array_almost_equal(f1(), f2(), decimal=6)
if __name__ == '__main__':
from theano.tests import main
main("test_shared_randomstreams")
class-attribute, instead of the __init__ function.
"""
rng1 = RandomStreams(1234)
rng2 = RandomStreams(2392)
assert rng1.state_updates is not rng2.state_updates
assert rng1.gen_seedgen is not rng2.gen_seedgen
def test_random_state_transfer(self):
"""
Test that random state can be transferred from one theano graph to another.
"""
class Graph:
def __init__(self, seed=123):
self.rng = RandomStreams(seed)
self.y = self.rng.uniform(size=(1, ))
g1 = Graph(seed=123)
f1 = function([], g1.y)
g2 = Graph(seed=987)
f2 = function([], g2.y)
for (su1, su2) in zip(g1.rng.state_updates, g2.rng.state_updates):
su2[0].set_value(su1[0].get_value())
numpy.testing.assert_array_almost_equal(f1(), f2(), decimal=6)
if __name__ == '__main__':
from theano.tests import main
main("test_shared_randomstreams")
