def test_gen_cloning_with_shape_change(self):
data = floatX(np.random.uniform(size=(1000, 10)))
minibatches = DataSampler(data, batchsize=50)
gen = generator(minibatches)
gen_r = tt_rng().normal(size=gen.shape).T
X = gen.dot(gen_r)
res, _ = theano.scan(lambda x: x.sum(), X, n_steps=X.shape[0])
assert res.eval().shape == (50, )
shared = theano.shared(data)
res2 = theano.clone(res, {gen: shared**2})
assert res2.eval().shape == (1000, )
def test_gen_cloning_with_shape_change(self):
data = floatX(np.random.uniform(size=(1000, 10)))
minibatches = DataSampler(data, batchsize=50)
gen = generator(minibatches)
gen_r = tt_rng().normal(size=gen.shape).T
X = gen.dot(gen_r)
res, _ = theano.scan(lambda x: x.sum(), X, n_steps=X.shape[0])
assert res.eval().shape == (50,)
shared = theano.shared(data)
res2 = theano.clone(res, {gen: shared**2})
assert res2.eval().shape == (1000,)
def randidx(self, size=None):
if size is None:
size = ()
elif isinstance(size, tt.TensorVariable):
if size.ndim < 1:
size = size[None]
elif size.ndim > 1:
raise ValueError('size ndim should be no more than 1d')
else:
pass
else:
size = tuple(np.atleast_1d(size))
return (tt_rng()
.uniform(size=size, low=0.0, high=self.histogram.shape[0] - 1e-16)
.astype('int64'))
def __init__(self,
vars=None,
batch_size=None,
total_size=None,
step_size=1.0,
model=None,
random_seed=None,
minibatches=None,
minibatch_tensors=None,
**kwargs):
warnings.warn(EXPERIMENTAL_WARNING)
model = modelcontext(model)
if vars is None:
vars = model.vars
vars = inputvars(vars)
self.model = model
self.vars = vars
self.batch_size = batch_size
self.total_size = total_size
_value_error(
total_size != None or batch_size != None,
"total_size and batch_size of training data have to be specified",
)
self.expected_iter = int(total_size / batch_size)
# set random stream
self.random = None
if random_seed is None:
self.random = tt_rng()
else:
self.random = tt_rng(random_seed)
self.step_size = step_size
shared = make_shared_replacements(vars, model)
self.updates = OrderedDict()
self.q_size = int(sum(v.dsize for v in self.vars))
flat_view = model.flatten(vars)
self.inarray = [flat_view.input]
self.dlog_prior = prior_dlogp(vars, model, flat_view)
self.dlogp_elemwise = elemwise_dlogL(vars, model, flat_view)
self.q_size = int(sum(v.dsize for v in self.vars))
if minibatch_tensors != None:
_check_minibatches(minibatch_tensors, minibatches)
self.minibatches = minibatches
# Replace input shared variables with tensors
def is_shared(t):
return isinstance(t, theano.compile.sharedvalue.SharedVariable)
tensors = [(t.type() if is_shared(t) else t)
for t in minibatch_tensors]
updates = OrderedDict({
t: t_
for t, t_ in zip(minibatch_tensors, tensors) if is_shared(t)
})
self.minibatch_tensors = tensors
self.inarray += self.minibatch_tensors
self.updates.update(updates)
self._initialize_values()
super().__init__(vars, shared)
def setup_method(self):
nr.seed(self.random_seed)
self.old_tt_rng = tt_rng()
set_tt_rng(RandomStream(self.random_seed))
