diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py index d7ab6083a7..f49b8510cd 100644 --- a/mindspore/common/tensor.py +++ b/mindspore/common/tensor.py @@ -252,22 +252,22 @@ class Tensor(Tensor_): @property def shape(self): - """The shape of tensor is a tuple.""" + """Returns the shape of the tensor as a tuple.""" return self._shape @property def dtype(self): - """The dtype of tensor is a mindspore type.""" + """Returns the dtype of the tensor (:class:`mindspore.dtype`).""" return self._dtype @property def size(self): - """The size reflects the total number of elements in tensor.""" + """Returns the total number of elements in tensor.""" return self._size @property def ndim(self): - """The ndim of tensor is an integer.""" + """Returns the number of tensor dimensions.""" return len(self._shape) @property @@ -277,22 +277,22 @@ class Tensor(Tensor_): @property def itemsize(self): - """The length of one tensor element in bytes.""" + """Returns the length of one tensor element in bytes.""" return self._itemsize @property def strides(self): - """The tuple of bytes to step in each dimension when traversing a tensor.""" + """Returns the tuple of bytes to step in each dimension when traversing a tensor.""" return self._strides @property def nbytes(self): - """The total number of bytes taken by the tensor.""" + """Returns the total number of bytes taken by the tensor.""" return self._nbytes @property def T(self): - """The transposed tensor.""" + """Returns the transposed tensor.""" return self.transpose() @property @@ -425,22 +425,21 @@ class Tensor(Tensor_): return tensor_operator_registry.get('mean')(keep_dims)(self, axis) def transpose(self, *axes): - """ - Returns a view of the array with axes transposed. + r""" + Returns a view of the tensor with axes transposed. - For a 1-D array this has no effect, as a transposed vector is simply the - same vector. For a 2-D array, this is a standard matrix transpose. For an - n-D array, if axes are given, their order indicates how the axes are permuted - (see Examples). If axes are not provided and a.shape = (i[0], i[1],... - i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]). + For a 1-D tensor this has no effect, as a transposed vector is simply the + same vector. For a 2-D tensor, this is a standard matrix transpose. For a + n-D tensor, if axes are given, their order indicates how the axes are permuted. + If axes are not provided and tensor.shape = (i[0], i[1],...i[n-2], i[n-1]), + then tensor.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]). Args: - axes(Union[None, tuple(int), list(int), n ints], optional): - None or no argument: reverses the order of the axes. - Tuple of ints: i in the j-th place in the tuple means a’s i-th - axis becomes a.transpose()’s j-th axis. - n ints: this form is intended simply as a `convenience alternative - to the tuple form. + axes(Union[None, tuple(int), list(int), \*int], optional): If axes is None or + blank, tensor.transpose() will reverse the order of the axes. If axes is tuple(int) + or list(int), tensor.transpose() will transpose the tensor to the new axes order. + If axes is \*int, this form is simply intended as a convenience alternative to the + tuple/list form. Returns: Tensor, has the same dimension as input tensor, with axes suitably permuted. @@ -451,17 +450,16 @@ class Tensor(Tensor_): def reshape(self, *shape): """ - Gives a new shape to an array without changing its data. + Gives a new shape to a tensor without changing its data. Args: shape(Union[int, tuple(int), list(int)]): 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. + 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. Returns: - reshaped_tensor(Tensor): This will be a new view object if possible; - otherwise, it will be a copy. + Tensor, with new specified shape. """ self.init_check() new_shape = validator.check_reshape_shp(shape) @@ -470,10 +468,9 @@ class Tensor(Tensor_): def ravel(self): """ Returns a contiguous flattened tensor. - A 1-D tensor, containing the elements of the input, is returned. Returns: - Tensor, has the same data type as x. + Tensor, a 1-D tensor, containing the same elements of the input. """ self.init_check() reshape_op = tensor_operator_registry.get('reshape')() @@ -481,15 +478,15 @@ class Tensor(Tensor_): def flatten(self, order='C'): """ - Returns a copy of the array collapsed into one dimension. + Returns a copy of the tensor collapsed into one dimension. Args: - order (str, optional): Can choose between `C` and `F`. `C` means to - flatten in row-major (C-style) order. ‘F’ means to flatten in column-major - (Fortran- style) order. Only `C` and `F` are supported. + order (str, optional): Can choose between \'C\' and \'F\'. \'C\' means to + flatten in row-major (C-style) order. \'F\' means to flatten in column-major + (Fortran- style) order. Only \'C\' and \'F\' are supported. Returns: - Tensor, has the same data type as x. + Tensor, has the same data type as input. """ self.init_check() reshape_op = tensor_operator_registry.get('reshape')() @@ -511,7 +508,7 @@ class Tensor(Tensor_): axis2 (int): Second axis. Returns: - Transposed tensor, has the same data type as the original tensor x. + Transposed tensor, has the same data type as the input. """ self.init_check() axis1, axis2 = validator.check_swapaxes_axis((axis1, axis2), self.ndim) @@ -534,10 +531,10 @@ class Tensor(Tensor_): def squeeze(self, axis=None): """ - Removes single-dimensional entries from the shape of an tensor. + Removes single-dimensional entries from the shape of a tensor. Args: - axis: Union[None, int, list(int), tuple(list)]. Default is None. + axis (Union[None, int, list(int), tuple(list)], optional): Default is None. Returns: Tensor, with all or a subset of the dimensions of length 1 removed. @@ -550,14 +547,13 @@ class Tensor(Tensor_): def astype(self, dtype, copy=True): """ - Returns a copy of the array, cast to a specified type. + Returns a copy of the tensor, casted to a specified type. Args: - dtype(Union[mstype.dtype, numpy.dtype, str]): Designated tensor dtype, - can be in format of np.float32, mstype.float32 or `float32`. Default - is mstype.float32. + dtype (Union[:class:`mindspore.dtype`, str]): Designated tensor dtype, can be in format + of :class:`mindspore.dtype.float32` or \'float32\'. Default is :class:`mindspore.dtype.float32` - copy(bool, optional): By default, astype always returns a newly allocated + copy (bool, optional): By default, astype always returns a newly allocated tensor. If this is set to false, the input tensor is returned instead of a copy if possible.