def float32_shared_constructor(value, name=None, strict=False,
allow_downcast=None, borrow=False, broadcastable=None):
"""SharedVariable Constructor for CudaNdarrayType from numpy.ndarray or CudaNdarray"""
# if value isn't a float32 ndarray, or a CudaNdarray then raise
if not isinstance(value, (numpy.ndarray, theano.sandbox.cuda.CudaNdarray)):
raise TypeError('ndarray or CudaNdarray required')
if isinstance(value, numpy.ndarray) and value.dtype.num != CudaNdarrayType.typenum:
raise TypeError('float32 ndarray required')
if broadcastable is None:
broadcastable = (False,) * len(value.shape)
type = CudaNdarrayType(broadcastable=broadcastable)
get_value_return_ndarray = True
if isinstance(value, theano.sandbox.cuda.CudaNdarray):
get_value_return_ndarray = False
if borrow:
deviceval = value
else:
deviceval = value.copy()
else:
# type.broadcastable is guaranteed to be a tuple, which this next
# function requires
deviceval = type_support_filter(value, type.broadcastable, False, None)
try:
rval = CudaNdarraySharedVariable(type=type, value=deviceval, name=name, strict=strict)
except Exception, e:
print "ERROR", e
raise
def float32_shared_constructor(value,
name=None,
strict=False,
allow_downcast=None,
borrow=False,
broadcastable=None,
target='gpu'):
"""
SharedVariable Constructor for CudaNdarrayType from numpy.ndarray or
CudaNdarray.
"""
if target != 'gpu':
raise TypeError('not for gpu')
if theano.sandbox.cuda.use.device_number is None:
theano.sandbox.cuda.use("gpu",
force=True,
default_to_move_computation_to_gpu=False,
move_shared_float32_to_gpu=False,
enable_cuda=False)
# if value isn't a float32 ndarray, or a CudaNdarray then raise
if not isinstance(value, (numpy.ndarray, theano.sandbox.cuda.CudaNdarray)):
raise TypeError('ndarray or CudaNdarray required')
if isinstance(
value,
numpy.ndarray) and value.dtype.num != CudaNdarrayType.typenum:
raise TypeError('float32 ndarray required')
if broadcastable is None:
broadcastable = (False, ) * len(value.shape)
type = CudaNdarrayType(broadcastable=broadcastable)
get_value_return_ndarray = True
if isinstance(value, theano.sandbox.cuda.CudaNdarray):
get_value_return_ndarray = False
if borrow:
deviceval = value
else:
deviceval = value.copy()
else:
# type.broadcastable is guaranteed to be a tuple, which this next
# function requires
deviceval = type_support_filter(value, type.broadcastable, False, None)
try:
rval = CudaNdarraySharedVariable(type=type,
value=deviceval,
name=name,
strict=strict)
except Exception as e:
print("ERROR", e)
raise
rval.get_value_return_ndarray = get_value_return_ndarray
return rval
def float32_shared_constructor(value, name=None, strict=False,
allow_downcast=None, borrow=False,
broadcastable=None, target='gpu'):
"""
SharedVariable Constructor for CudaNdarrayType from numpy.ndarray or
CudaNdarray.
"""
if target != 'gpu':
raise TypeError('not for gpu')
if theano.sandbox.cuda.use.device_number is None:
theano.sandbox.cuda.use("gpu",
force=True,
default_to_move_computation_to_gpu=False,
move_shared_float32_to_gpu=False,
enable_cuda=False)
# if value isn't a float32 ndarray, or a CudaNdarray then raise
if not isinstance(value, (numpy.ndarray, theano.sandbox.cuda.CudaNdarray)):
raise TypeError('ndarray or CudaNdarray required')
if isinstance(value, numpy.ndarray) and value.dtype.num != CudaNdarrayType.typenum:
raise TypeError('float32 ndarray required')
if broadcastable is None:
broadcastable = (False,) * len(value.shape)
type = CudaNdarrayType(broadcastable=broadcastable)
get_value_return_ndarray = True
if isinstance(value, theano.sandbox.cuda.CudaNdarray):
get_value_return_ndarray = False
if borrow:
deviceval = value
else:
deviceval = value.copy()
else:
# type.broadcastable is guaranteed to be a tuple, which this next
# function requires
deviceval = type_support_filter(value, type.broadcastable, False, None)
try:
rval = CudaNdarraySharedVariable(type=type, value=deviceval, name=name, strict=strict)
except Exception as e:
print("ERROR", e)
raise
rval.get_value_return_ndarray = get_value_return_ndarray
return rval
def float32_shared_constructor(value,
name=None,
strict=False,
allow_downcast=None,
borrow=False,
broadcastable=None):
"""SharedVariable Constructor for CudaNdarrayType from numpy.ndarray or CudaNdarray"""
# if value isn't a float32 ndarray, or a CudaNdarray then raise
if not isinstance(value, (numpy.ndarray, theano.sandbox.cuda.CudaNdarray)):
raise TypeError('ndarray or CudaNdarray required')
if isinstance(
value,
numpy.ndarray) and value.dtype.num != CudaNdarrayType.typenum:
raise TypeError('float32 ndarray required')
if broadcastable is None:
broadcastable = (False, ) * len(value.shape)
type = CudaNdarrayType(broadcastable=broadcastable)
get_value_return_ndarray = True
if isinstance(value, theano.sandbox.cuda.CudaNdarray):
get_value_return_ndarray = False
if borrow:
deviceval = value
else:
deviceval = value.copy()
else:
# type.broadcastable is guaranteed to be a tuple, which this next
# function requires
deviceval = type_support_filter(value, type.broadcastable, False, None)
try:
rval = CudaNdarraySharedVariable(type=type,
value=deviceval,
name=name,
strict=strict)
except Exception, e:
print "ERROR", e
raise
