pymc.math.stack#
- pymc.math.stack(tensors,axis=0)[source]#
Stack tensors in sequence on given axis (default is 0).
Take a sequence of tensors or tensor-like constant and stack them ongiven axis to make a single tensor. The size in dimensionaxis of theresult will be equal to the number of tensors passed.
- Parameters:
- tensors
Sequence[TensorLike] A list of tensors or tensor-like constants to be stacked.
- axis
int The index of the new axis. Default value is 0.
- tensors
Examples
>>>a=pytensor.tensor.type.scalar()>>>b=pytensor.tensor.type.scalar()>>>c=pytensor.tensor.type.scalar()>>>x=pytensor.tensor.stack([a,b,c])>>>x.ndim# x is a vector of length 3.1>>>a=pytensor.tensor.type.tensor4()>>>b=pytensor.tensor.type.tensor4()>>>c=pytensor.tensor.type.tensor4()>>>x=pytensor.tensor.stack([a,b,c])>>>x.ndim# x is a 5d tensor.5>>>rval=x.eval(dict((t,np.zeros((2,2,2,2)))fortin[a,b,c]))>>>rval.shape# 3 tensors are stacked on axis 0(3, 2, 2, 2, 2)>>>x=pytensor.tensor.stack([a,b,c],axis=3)>>>x.ndim5>>>rval=x.eval(dict((t,np.zeros((2,2,2,2)))fortin[a,b,c]))>>>rval.shape# 3 tensors are stacked on axis 3(2, 2, 2, 3, 2)>>>x=pytensor.tensor.stack([a,b,c],axis=-2)>>>x.ndim5>>>rval=x.eval(dict((t,np.zeros((2,2,2,2)))fortin[a,b,c]))>>>rval.shape# 3 tensors are stacked on axis -2(2, 2, 2, 3, 2)