def apply(self, fgraph):
did_something = True
while did_something:
nodelist = fgraph.toposort()
did_something = False
for node in nodelist:
if node.op == T._max_and_argmax:
if len(node.outputs[1].clients)==0:
try:
axis=get_scalar_constant_value(node.inputs[1])
except NotScalarConstantError:
return False
new = CAReduce(scal.maximum,axis)(node.inputs[0])
try:
fgraph.replace_all_validate(
((node.outputs[0],new),),
reason = self.__class__.__name__)
did_something = True
break
except InconsistencyError, e:
pass
def make_node(self, x, repeats):
x = basic.as_tensor_variable(x)
repeats = basic.as_tensor_variable(repeats)
if repeats.dtype not in tensor.discrete_dtypes:
raise TypeError("repeats.dtype must be an integer.")
# Some dtypes are not supported by numpy's implementation of repeat.
# Until another one is available, we should fail at graph construction
# time, not wait for execution.
int_bitwidth = theano.gof.cmodule.python_int_bitwidth()
if int_bitwidth == 64:
numpy_unsupported_dtypes = ('uint64',)
if int_bitwidth == 32:
numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
if repeats.dtype in numpy_unsupported_dtypes:
raise TypeError(
("dtypes %s are not supported by numpy.repeat "
"for the 'repeats' parameter, "
% numpy_unsupported_dtypes), repeats.dtype)
if self.axis is None:
broadcastable=[False]
else:
try:
const_reps = basic.get_scalar_constant_value(repeats)
except basic.NotScalarConstantError:
const_reps = None
if const_reps == 1:
broadcastable = x.broadcastable
else:
broadcastable = list(x.broadcastable)
broadcastable[self.axis] = False
out_type = theano.tensor.TensorType(x.dtype, broadcastable)
return theano.Apply(self, [x, repeats], [out_type()])