def __init__(self, type, data, name=None):
Constant.__init__(self, type, data, name)
if (isinstance(data, numpy.ndarray) and data.ndim > 0
and len(numpy.unique(data)) == 1):
self.tag.unique_value = numpy.unique(data)[0]
else:
self.tag.unique_value = None
def __init__(self, type, data, name=None):
Constant.__init__(self, type, data, name)
if (isinstance(data, numpy.ndarray) and
data.ndim > 0 and
len(numpy.unique(data)) == 1):
self.tag.unique_value = numpy.unique(data)[0]
else:
self.tag.unique_value = None
def __init__(self, type, data, name=None):
Constant.__init__(self, type, data, name)
self.tag.unique_value = None
if isinstance(data, numpy.ndarray) and data.ndim > 0:
flat_data = data.ravel()
if flat_data.shape[0]:
if (flat_data == flat_data[0]).all():
self.tag.unique_value = flat_data[0]
def __init__(self, type, data, name=None):
Constant.__init__(self, type, data, name)
self.tag.unique_value = None
if isinstance(data, np.ndarray) and data.ndim > 0:
flat_data = data.ravel()
if flat_data.shape[0]:
if (flat_data == flat_data[0]).all():
self.tag.unique_value = flat_data[0]
def __init__(self, type, data, name=None):
assert isinstance(data, slice)
# Numpy ndarray aren't hashable, so get rid of them.
if isinstance(data.start, numpy.ndarray):
assert data.start.ndim == 0
assert "int" in str(data.start.dtype)
data = slice(int(data.start), data.stop, data.step)
elif isinstance(data.stop, numpy.ndarray):
assert data.stop.ndim == 0
assert "int" in str(data.stop.dtype)
data = slice(data.start, int(data.stop), data.step)
elif isinstance(data.step, numpy.ndarray):
assert data.step.ndim == 0
assert "int" in str(data.step.dtype)
data = slice(data.start, int(data.stop), data.step)
Constant.__init__(self, type, data, name)
def __init__(self, type, data, name=None):
assert isinstance(data, slice)
# Numpy ndarray aren't hashable, so get rid of them.
if isinstance(data.start, np.ndarray):
assert data.start.ndim == 0
assert str(data.start.dtype) in theano.tensor.integer_dtypes
data = slice(int(data.start), data.stop, data.step)
elif isinstance(data.stop, np.ndarray):
assert data.stop.ndim == 0
assert str(data.stop.dtype) in theano.tensor.integer_dtypes
data = slice(data.start, int(data.stop), data.step)
elif isinstance(data.step, np.ndarray):
assert data.step.ndim == 0
assert str(data.step.dtype) in theano.tensor.integer_dtypes
data = slice(data.start, int(data.stop), data.step)
Constant.__init__(self, type, data, name)
def test_none_Constant():
""" Tests equals
We had an error in the past with unpickling
"""
o1 = Constant(NoneTypeT(), None, name='NoneConst')
o2 = Constant(NoneTypeT(), None, name='NoneConst')
assert o1.equals(o2)
assert NoneConst.equals(o1)
assert o1.equals(NoneConst)
assert NoneConst.equals(o2)
assert o2.equals(NoneConst)
# This trigger equals that returned the wrong answer in the past.
import six.moves.cPickle as pickle
import theano
from theano import tensor
x = tensor.vector('x')
y = tensor.argmax(x)
kwargs = {}
# We can't pickle DebugMode
if theano.config.mode in ["DebugMode", "DEBUG_MODE"]:
kwargs = {'mode': 'FAST_RUN'}
f = theano.function([x], [y], **kwargs)
pickle.loads(pickle.dumps(f))
def make_node(self, x, index):
assert isinstance(x.type, TypedListType)
if not isinstance(index, Variable):
if isinstance(index, slice):
index = Constant(SliceType(), index)
return Apply(self, [x, index], [x.type()])
else:
index = T.constant(index, ndim=0)
return Apply(self, [x, index], [x.ttype()])
if isinstance(index.type, SliceType):
return Apply(self, [x, index], [x.type()])
elif isinstance(index, T.TensorVariable) and index.ndim == 0:
return Apply(self, [x, index], [x.ttype()])
else:
raise TypeError('Expected scalar or slice as index.')
def test_none_Constant():
""" Tests equals
We had an error in the past with unpickling
"""
o1 = Constant(NoneTypeT(), None, name='NoneConst')
o2 = Constant(NoneTypeT(), None, name='NoneConst')
assert o1.equals(o2)
assert NoneConst.equals(o1)
assert o1.equals(NoneConst)
assert NoneConst.equals(o2)
assert o2.equals(NoneConst)
# This trigger equals that returned the wrong answer in the past.
import cPickle
import theano
from theano import tensor
x = tensor.vector('x')
y = tensor.argmax(x)
f = theano.function([x], [y])
cPickle.loads(cPickle.dumps(f))
def make_node(self, path):
if isinstance(path, str):
path = Constant(Generic(), path)
return gof.Apply(
self, [path],
[tensor(self.dtype, broadcastable=self.broadcastable)])
self.data.stop, self.data.step)
SliceType.Constant = SliceConstant
class NoneTypeT(Generic):
"""
Inherit from Generic to have c code working.
"""
def filter(self, x, strict=False, allow_downcast=None):
if x is None:
return x
else:
raise TypeError('Expected None!')
@staticmethod
def may_share_memory(a, b):
# None never share memory between object, in the sence of DebugMode.
# Python None are singleton
return False
none_type_t = NoneTypeT()
# This is a variable instance. It can be used only once per fgraph.
# So use NoneConst.clone() before using it in a Theano graph.
# Use NoneConst.equals(x) to check if two variable are NoneConst.
NoneConst = Constant(none_type_t, None, name='NoneConst')
def __str__(self):
return "slice"
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hashtype(self)
slicetype = SliceType()
class NoneTypeT(Generic):
"""
Inherit from Generic to have c code working.
"""
def filter(self, x, strict=False, allow_downcast=None):
if x is None:
return x
else:
raise TypeError('Expected None!')
none_type_t = NoneTypeT()
# This is a variable instance. It can be used only once per fgraph.
# So use NoneConst.clone() before using it in a Theano graph.
# Use NoneConst.equal(x) to check if two variable are NoneConst.
NoneConst = Constant(NoneTypeT(), None, name='NoneConst')
)
SliceType.Constant = SliceConstant
class NoneTypeT(Generic):
"""
Inherit from Generic to have c code working.
"""
def filter(self, x, strict=False, allow_downcast=None):
if x is None:
return x
else:
raise TypeError("Expected None!")
@staticmethod
def may_share_memory(a, b):
# None never share memory between object, in the sence of DebugMode.
# Python None are singleton
return False
none_type_t = NoneTypeT()
# This is a variable instance. It can be used only once per fgraph.
# So use NoneConst.clone() before using it in a Theano graph.
# Use NoneConst.equals(x) to check if two variable are NoneConst.
NoneConst = Constant(none_type_t, None, name="NoneConst")
