mindspore.Tensor.view

Tensor.view(*shape) → Tensor

Reshape the tensor according to the input shape .

Parameters

shape (Union[tuple(int), int]) – Dimension of the output tensor.

Returns

Tensor, which dimension is the input shape's value.

Examples

>>> from mindspore import Tensor
>>> import numpy as np
>>> a = Tensor(np.array([[1, 2, 3], [2, 3, 4]], dtype=np.float32))
>>> output = a.view((3, 2))
>>> print(output)
[[1. 2.]
 [3. 2.]
 [3. 4.]]

Tensor.view(dtype) → Tensor

Returns a new tensor with the same data as input but of a different dtype.

Note

• If the element size of dtype is different from that of input's dtype, the input must meet following conditions:

  • Shape of input can't be empty, which means input can't be a scalar tensor.

  • Last stride of input must be 1 .

• If the element size of dtype is greater than that of input's dtype, the input must also meet following conditions:

  • Last dimension of input shape must be divisible by the ratio between the element sizes of the dtype and input's dtype.

  • The storage_offset of input must be divisible by the ratio between the element sizes of the dtype and input's dtype.

  • The strides of all dimensions without the last dimension, must be divisible by the ratio between the element sizes of the dtype and input's dtype.

• Only support PyNative mode.

Parameters

dtype (mindspore.dtype) – The desired data type of returned tensor.

Returns

Tensor, which has same data as input and desired data type.

Examples

>>> import mindspore as ms
>>> import numpy as np
>>> a = ms.Tensor(np.array([[1, 2], [3, 4]]), dtype=ms.int64)
>>> output = a.view(ms.int32)
>>> print(output)
[[1 0 2 0]
 [3 0 4 0]]