def test_create_numpy_strict_false(self):
# here the value is perfect, and we're not strict about it,
# so creation should work
SharedVariable(name='u',
type=Tensor(broadcastable=[False], dtype='float64'),
value=numpy.asarray([1., 2.]),
strict=False)
# here the value is castable, and we're not strict about it,
# so creation should work
SharedVariable(name='u',
type=Tensor(broadcastable=[False], dtype='float64'),
value=[1., 2.],
strict=False)
# here the value is castable, and we're not strict about it,
# so creation should work
SharedVariable(
name='u',
type=Tensor(broadcastable=[False], dtype='float64'),
value=[1, 2], # different dtype and not a numpy array
strict=False)
# here the value is not castable, and we're not strict about it,
# this is beyond strictness, it must fail
try:
SharedVariable(
name='u',
type=Tensor(broadcastable=[False], dtype='float64'),
value=dict(), # not an array by any stretch
strict=False)
assert 0
except TypeError:
pass
def test_use_numpy_strict_false(self):
# here the value is perfect, and we're not strict about it,
# so creation should work
u = SharedVariable(name='u',
type=Tensor(broadcastable=[False], dtype='float64'),
value=numpy.asarray([1., 2.]),
strict=False)
# check that assignments to value are cast properly
u.set_value([3, 4])
assert type(u.get_value()) is numpy.ndarray
assert str(u.get_value(borrow=True).dtype) == 'float64'
assert numpy.all(u.get_value() == [3, 4])
# check that assignments of nonsense fail
try:
u.set_value('adsf')
assert 0
except ValueError:
pass
# check that an assignment of a perfect value results in no copying
uval = theano._asarray([5, 6, 7, 8], dtype='float64')
u.set_value(uval, borrow=True)
assert u.get_value(borrow=True) is uval
