giagrad.Tensor.reshape#

Tensor.reshape(*newshape) → Tensor[source]#

Returns a new tensor with shape equals newshape.

When possible, the returned tensor will be a view of the input. Otherwise, it will be a copy. Contiguous inputs and inputs with compatible strides can be reshaped without copying, but you should not depend on the copying vs. viewing behavior.

Parameters:: *newshape¶ (list of ints) – The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions.

Examples

>>> a = Tensor.empty(6).ones()
>>> a.reshape(2, 3)
tensor: [[1. 1. 1.]
         [1. 1. 1.]] fn: Reshape
>>> b = Tensor.empty(2, 2, 2).zeros()
>>> b.reshape(2, -1)
tensor: [[0. 0. 0. 0.]
         [0. 0. 0. 0.]] fn: Reshape