forked from mindspore-Ecosystem/mindspore
update API in tensor.py
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yanglf1121
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4364abc7ee
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5b986fa419
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@ -252,22 +252,22 @@ class Tensor(Tensor_):
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@property
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def shape(self):
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"""The shape of tensor is a tuple."""
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"""Returns the shape of the tensor as a tuple."""
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return self._shape
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@property
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def dtype(self):
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"""The dtype of tensor is a mindspore type."""
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"""Returns the dtype of the tensor (:class:`mindspore.dtype`)."""
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return self._dtype
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@property
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def size(self):
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"""The size reflects the total number of elements in tensor."""
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"""Returns the total number of elements in tensor."""
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return self._size
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@property
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def ndim(self):
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"""The ndim of tensor is an integer."""
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"""Returns the number of tensor dimensions."""
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return len(self._shape)
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@property
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@ -277,22 +277,22 @@ class Tensor(Tensor_):
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@property
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def itemsize(self):
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"""The length of one tensor element in bytes."""
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"""Returns the length of one tensor element in bytes."""
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return self._itemsize
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@property
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def strides(self):
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"""The tuple of bytes to step in each dimension when traversing a tensor."""
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"""Returns the tuple of bytes to step in each dimension when traversing a tensor."""
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return self._strides
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@property
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def nbytes(self):
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"""The total number of bytes taken by the tensor."""
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"""Returns the total number of bytes taken by the tensor."""
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return self._nbytes
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@property
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def T(self):
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"""The transposed tensor."""
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"""Returns the transposed tensor."""
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return self.transpose()
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@property
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@ -425,22 +425,21 @@ class Tensor(Tensor_):
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return tensor_operator_registry.get('mean')(keep_dims)(self, axis)
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def transpose(self, *axes):
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"""
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Returns a view of the array with axes transposed.
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r"""
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Returns a view of the tensor with axes transposed.
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For a 1-D array this has no effect, as a transposed vector is simply the
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same vector. For a 2-D array, this is a standard matrix transpose. For an
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n-D array, if axes are given, their order indicates how the axes are permuted
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(see Examples). If axes are not provided and a.shape = (i[0], i[1],...
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i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]).
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For a 1-D tensor this has no effect, as a transposed vector is simply the
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same vector. For a 2-D tensor, this is a standard matrix transpose. For a
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n-D tensor, if axes are given, their order indicates how the axes are permuted.
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If axes are not provided and tensor.shape = (i[0], i[1],...i[n-2], i[n-1]),
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then tensor.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]).
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Args:
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axes(Union[None, tuple(int), list(int), n ints], optional):
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None or no argument: reverses the order of the axes.
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Tuple of ints: i in the j-th place in the tuple means a’s i-th
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axis becomes a.transpose()’s j-th axis.
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n ints: this form is intended simply as a `convenience alternative
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to the tuple form.
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axes(Union[None, tuple(int), list(int), \*int], optional): If axes is None or
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blank, tensor.transpose() will reverse the order of the axes. If axes is tuple(int)
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or list(int), tensor.transpose() will transpose the tensor to the new axes order.
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If axes is \*int, this form is simply intended as a convenience alternative to the
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tuple/list form.
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Returns:
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Tensor, has the same dimension as input tensor, with axes suitably permuted.
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@ -451,17 +450,16 @@ class Tensor(Tensor_):
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def reshape(self, *shape):
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"""
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Gives a new shape to an array without changing its data.
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Gives a new shape to a tensor without changing its data.
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Args:
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shape(Union[int, tuple(int), list(int)]): The new shape should be compatible
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with the original shape. If an integer, then the result will be a 1-D
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array of that length. One shape dimension can be -1. In this case, the
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value is inferred from the length of the array and remaining dimensions.
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with the original shape. If an integer, then the result will be a 1-D
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array of that length. One shape dimension can be -1. In this case, the
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value is inferred from the length of the array and remaining dimensions.
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Returns:
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reshaped_tensor(Tensor): This will be a new view object if possible;
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otherwise, it will be a copy.
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Tensor, with new specified shape.
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"""
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self.init_check()
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new_shape = validator.check_reshape_shp(shape)
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@ -470,10 +468,9 @@ class Tensor(Tensor_):
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def ravel(self):
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"""
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Returns a contiguous flattened tensor.
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A 1-D tensor, containing the elements of the input, is returned.
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Returns:
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Tensor, has the same data type as x.
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Tensor, a 1-D tensor, containing the same elements of the input.
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"""
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self.init_check()
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reshape_op = tensor_operator_registry.get('reshape')()
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@ -481,15 +478,15 @@ class Tensor(Tensor_):
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def flatten(self, order='C'):
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"""
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Returns a copy of the array collapsed into one dimension.
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Returns a copy of the tensor collapsed into one dimension.
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Args:
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order (str, optional): Can choose between `C` and `F`. `C` means to
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flatten in row-major (C-style) order. ‘F’ means to flatten in column-major
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(Fortran- style) order. Only `C` and `F` are supported.
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order (str, optional): Can choose between \'C\' and \'F\'. \'C\' means to
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flatten in row-major (C-style) order. \'F\' means to flatten in column-major
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(Fortran- style) order. Only \'C\' and \'F\' are supported.
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Returns:
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Tensor, has the same data type as x.
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Tensor, has the same data type as input.
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"""
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self.init_check()
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reshape_op = tensor_operator_registry.get('reshape')()
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@ -511,7 +508,7 @@ class Tensor(Tensor_):
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axis2 (int): Second axis.
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Returns:
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Transposed tensor, has the same data type as the original tensor x.
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Transposed tensor, has the same data type as the input.
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"""
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self.init_check()
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axis1, axis2 = validator.check_swapaxes_axis((axis1, axis2), self.ndim)
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@ -534,10 +531,10 @@ class Tensor(Tensor_):
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def squeeze(self, axis=None):
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"""
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Removes single-dimensional entries from the shape of an tensor.
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Removes single-dimensional entries from the shape of a tensor.
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Args:
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axis: Union[None, int, list(int), tuple(list)]. Default is None.
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axis (Union[None, int, list(int), tuple(list)], optional): Default is None.
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Returns:
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Tensor, with all or a subset of the dimensions of length 1 removed.
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@ -550,14 +547,13 @@ class Tensor(Tensor_):
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def astype(self, dtype, copy=True):
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"""
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Returns a copy of the array, cast to a specified type.
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Returns a copy of the tensor, casted to a specified type.
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Args:
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dtype(Union[mstype.dtype, numpy.dtype, str]): Designated tensor dtype,
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can be in format of np.float32, mstype.float32 or `float32`. Default
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is mstype.float32.
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dtype (Union[:class:`mindspore.dtype`, str]): Designated tensor dtype, can be in format
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of :class:`mindspore.dtype.float32` or \'float32\'. Default is :class:`mindspore.dtype.float32`
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copy(bool, optional): By default, astype always returns a newly allocated
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copy (bool, optional): By default, astype always returns a newly allocated
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tensor. If this is set to false, the input tensor is returned instead
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of a copy if possible.
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