mindspore.Tensor.flatten
- Tensor.flatten(start_dim=0, end_dim=- 1) Tensor[source]
Flatten a tensor along dimensions from start_dim to end_dim.
- Parameters
- Returns
Tensor. If no dimensions are flattened, returns the original self, otherwise return the flattened Tensor. If self is a 0-dimensional Tensor, a 1-dimensional Tensor will be returned.
- Raises
TypeError – If start_dim or end_dim is not int.
ValueError – If start_dim is greater than end_dim after canonicalized.
ValueError – If start_dim or end_dim is not in range of [-self.dim, self.dim-1].
- Supported Platforms:
AscendGPUCPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor >>> input_x = Tensor(np.ones(shape=[1, 2, 3, 4]), mindspore.float32) >>> output = input_x.flatten(0, -1) >>> print(output.shape) (24,)
Flatten a tensor along dimensions from start_dim to start_dim.
- Parameters
order (str, optional) – Only
'C'and'F'are supported.'C'means to flatten in row-major (C-style) order.'F'means to flatten in column-major (Fortran-style) order. Default:'C'.- Keyword Arguments
- Returns
Tensor. If no dimensions are flattened, returns the original self, otherwise return the flattened Tensor. If self is a 0-dimensional Tensor, a 1-dimensional Tensor will be returned.
- Raises
TypeError – If order is not string type.
ValueError – If order is string type, but not
'C'or'F'.TypeError – If start_dim or end_dim is not int.
ValueError – If start_dim is greater than end_dim after canonicalized.
ValueError – If start_dim or end_dim is not in range of [-self.dim, self.dim-1].
- Supported Platforms:
AscendGPUCPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor >>> input_x = Tensor(np.ones(shape=[1, 2, 3, 4]), mindspore.float32) >>> output = input_x.flatten(order='C') >>> print(output.shape) (24,)