mindspore.Tensor
- class mindspore.Tensor(input_data=None, dtype=None, shape=None, init=None)[source]
Tensor is a data structure that stores an n-dimensional array.
- Parameters
input_data (Union[Tensor, float, int, bool, tuple, list, numpy.ndarray]) – The data to be stored. It can be another Tensor, Python number or NumPy ndarray. Default: None.
dtype (
mindspore.dtype
) – Used to indicate the data type of the output Tensor. The argument should be defined in mindspore.dtype. If it is None, the data type of the output Tensor will be the same as the input_data. Default: None.shape (Union[tuple, list, int]) – Used to indicate the shape of the output Tensor. The argument should be a list of integers, a tuple of integers or an integer. If input_data is available, shape doesn’t need to be set. Default: None.
init (Initializer) – The information of init data. ‘init’ is used for delayed initialization in parallel mode. Usually, it is not recommended to use ‘init’ interface to initialize Tensor in the other conditions. If ‘init’ interface is used to initialize Tensor, the Tensor.init_data API needs to be called to convert Tensor to the actual data. Default: None.
- Outputs:
Tensor.
Examples
>>> import numpy as np >>> import mindspore as ms >>> from mindspore import Tensor >>> from mindspore.common.initializer import One >>> # initialize a tensor with numpy.ndarray >>> t1 = Tensor(np.zeros([1, 2, 3]), ms.float32) >>> print(t1) [[[0. 0. 0.] [0. 0. 0.]]] >>> print(type(t1)) <class 'mindspore.common.tensor.Tensor'> >>> print(t1.shape) (1, 2, 3) >>> print(t1.dtype) Float32 >>> >>> # initialize a tensor with a float scalar >>> t2 = Tensor(0.1) >>> print(t2) 0.1 >>> print(type(t2)) <class 'mindspore.common.tensor.Tensor'> >>> print(t2.shape) () >>> print(t2.dtype) Float32 >>> >>> # initialize a tensor with a tuple >>> t3 = Tensor((1, 2)) >>> print(t3) [1 2] >>> print(type(t3)) <class 'mindspore.common.tensor.Tensor'> >>> print(t3.shape) (2,) >>> print(t3.dtype) Int64 ... >>> # initialize a tensor with init >>> t4 = Tensor(shape = (1, 3), dtype=ms.float32, init=One()) >>> print(t4) [[1. 1. 1.]] >>> print(type(t4)) <class 'mindspore.common.tensor.Tensor'> >>> print(t4.shape) (1, 3) >>> print(t4.dtype) Float32
- property T
Return the transposed tensor.
- abs()[source]
Return absolute value element-wisely.
- Returns
Tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> from mindspore import Tensor >>> a = Tensor([1.1, -2.1]).astype("float32") >>> output = a.abs() >>> print(output) [1.1 2.1]
- all(axis=(), keep_dims=False)[source]
Check all tensor elements along a given axis evaluate to True.
- Parameters
axis (Union[None, int, tuple(int)]) – Dimensions of reduction. When the axis is None or empty tuple, reduce all dimensions. When the axis is int or tuple(int), if the dimension of Tensor is dim, the value range is [-dim, dim). Default: ().
keep_dims (bool) – Whether to keep the reduced dimensions. Default: False.
- Returns
Tensor, if all tensor elements along the given axis evaluate to True, its value is True, otherwise its value is False. If the axis is None or empty tuple, reduce all dimensions.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.any()
: Check any tensor element along a given axis evaluate to True.Examples
>>> from mindspore import Tensor >>> a = Tensor([True, True, False]) >>> output = a.all() >>> print(output) False
- any(axis=(), keep_dims=False)[source]
Check any tensor element along a given axis evaluate to True.
- Parameters
axis (Union[None, int, tuple(int)]) – Dimensions of reduction. When the axis is None or empty tuple, reduce all dimensions. When the axis is int or tuple(int), if the dimension of Tensor is dim, the value range is [-dim, dim). Default: ().
keep_dims (bool) – Whether to keep the reduced dimensions. Default: False.
- Returns
Tensor, if any tensor element along the given axis evaluates to True, its value is True, otherwise its value is False. If the axis is None or empty tuple, reduce all dimensions.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.all()
: Check all tensor elements along a given axis evaluate to True.Examples
>>> from mindspore import Tensor >>> a = Tensor([True, True, False]) >>> output = a.any() >>> print(output) True
- argmax(axis=None)[source]
Return the indices of the maximum values along an axis.
- Parameters
axis (int, optional) – By default, the index is into the flattened tensor, otherwise along the specified axis. Default: None.
- Returns
Tensor, indices into the input tensor. It has the same shape as self.shape with the dimension along axis removed.
- Raises
ValueError – If the axis is out of range.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.argmin()
: Return the indices of the minimum values along an axis.mindspore.Tensor.min()
: Return the minimum of a tensor or minimum along an axis.mindspore.Tensor.max()
: Return the maximum of a tensor or maximum along an axis.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> a = Tensor(np.arange(10, 16).reshape(2, 3).astype("float32")) >>> print(a.argmax()) 5
- argmin(axis=None)[source]
Return the indices of the minimum values along an axis.
- Parameters
axis (int, optional) – By default, the index is into the flattened tensor, otherwise along the specified axis. Default: None.
- Returns
Tensor, indices into the input tensor. It has the same shape as self.shape with the dimension along axis removed.
- Raises
ValueError – If the axis is out of range.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.argmax()
: Return the indices of the maximum values along an axis.mindspore.Tensor.min()
: Return the minimum of a tensor or minimum along an axis.mindspore.Tensor.max()
: Return the maximum of a tensor or maximum along an axis.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> a = Tensor(np.arange(10, 16).reshape(2, 3).astype("float32")) >>> print(a.argmin()) 0
- asnumpy()[source]
Convert tensor to numpy array. Returns self tensor as a NumPy ndarray. This tensor and the returned ndarray share the same underlying storage. Changes to self tensor will be reflected in the ndarray.
- Returns
A numpy ndarray which shares the same underlying storage with the tensor.
Examples
>>> from mindspore import Tensor >>> import numpy as np >>> x = Tensor(np.array([1, 2], dtype=np.float32)) >>> y = x.asnumpy() >>> y[0] = 11 >>> print(x) [11. 2.] >>> print(y) [11. 2.]
- astype(dtype, copy=True)[source]
Return a copy of the tensor, cast to a specified type.
- Parameters
dtype (Union[
mindspore.dtype
, str]) – Designated tensor dtype, can be in format ofmindspore.dtype.float32
or float32.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. Default: True.
- Returns
Tensor, with the designated dtype.
- Raises
TypeError – If the specified dtype cannot be understood.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.ones((1,2,2,1), dtype=np.float32)) >>> x = x.astype("int32") >>> print(x.dtype) Int32
- choose(choices, mode='clip')[source]
Construct a tensor from an index tensor and a list of tensors to choose from.
- Parameters
choices (Union[tuple, list, Tensor]) – Choice tensors. The input tensor and all of the choices must be broadcasted to the same shape. If choices is itself a tensor, then its outermost dimension (i.e., the one corresponding to
choices.shape[0]
) is taken as defining the “sequence”.mode ('raise', 'wrap', 'clip', optional) –
Specifies how indices outside
[0, n-1]
will be treated:’raise’ – Raises an error;
’wrap’ – Wraps around;
’clip’ – Clips to the range. ‘clip’ mode means that values greater than n-1 are mapped to n-1. Note that this disables indexing with negative numbers.
Default: ‘clip’.
- Returns
Tensor, the merged result.
- Supported Platforms:
Ascend
GPU
CPU
- Raises
ValueError – If the input tensor and any of the choices cannot be broadcast.
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33]] >>> x = Tensor(np.array([2, 3, 1, 0])) >>> print(x.choose(choices)) [20 31 12 3]
- clip(xmin, xmax, dtype=None)[source]
Clips (limits) the values in a Tensor.
Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of \([0, 1]\) is specified, values smaller than 0 become 0, and values larger than 1 become 1.
Note
Currently, clip with xmin=nan or xmax=nan is not supported.
- Parameters
xmin (Tensor, scalar, None) – Minimum value. If None, clipping is not performed on the lower interval edge. Not more than one of xmin and xmax may be None.
xmax (Tensor, scalar, None) – Maximum value. If None, clipping is not performed on the upper interval edge. Not more than one of xmin and xmax may be None. If xmin or xmax are tensors, then xmin, xmax and the given tensor will be broadcasted to match their shapes.
dtype (
mindspore.dtype
, optional) – Overrides the dtype of the output Tensor. Default is None.
- Returns
Tensor, a tensor with the elements of the input tensor, but where values < xmin are replaced with xmin, and those > xmax with xmax.
- Raises
TypeError – If inputs have types not specified above.
ValueError – If the shapes of x1 and x2 cannot broadcast, or both xmin and xmax are None.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> from mindspore import Tensor >>> x = Tensor([1, 2, 3, -4, 0, 3, 2, 0]).astype("float32") >>> y = x.clip(0, 2) >>> print(y) [1. 2. 2. 0. 0. 2. 2. 0.] >>> t = Tensor([1, 1, 1, 1, 1, 1, 1, 1]) >>> y = x.clip(t, 2) >>> print(y) [1. 2. 2. 1. 1. 2. 2. 1.]
- copy()[source]
Return a copy of the tensor.
Note
The current implementation does not support order argument.
- Returns
Copied tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> a = Tensor(np.ones((3,3)).astype("float32")) >>> output = a.copy() >>> print(output) [[1. 1. 1.] [1. 1. 1.] [1. 1. 1.]]
- cumsum(axis=None, dtype=None)[source]
Return the cumulative sum of the elements along a given axis.
Note
If
self.dtype
isint8
,int16
orbool
, the result dtype will be elevated toint32
,int64
is not supported.- Parameters
axis (int, optional) – Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array.
dtype (
mindspore.dtype
, optional) – If not specified, stay the same as original tensor, unless it has an integer dtype with a precision less thanfloat32
. In that case,float32
is used. Default: None.
- Raises
ValueError – If the axis is out of range.
- Returns
Tensor.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.sum()
: Return sum of tensor elements over a given axis.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> a = Tensor(np.ones((3,3)).astype("float32")) >>> output = a.cumsum(axis=0) >>> print(output) [[1. 1. 1.] [2. 2. 2.] [3. 3. 3.]]
- diagonal(offset=0, axis1=0, axis2=1)[source]
Return specified diagonals.
- Parameters
offset (int, optional) – Offset of the diagonal from the main diagonal. Can be positive or negative. Defaults to main diagonal.
axis1 (int, optional) – Axis to be used as the first axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults to first axis (0).
axis2 (int, optional) – Axis to be used as the second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults to second axis.
- Returns
Tensor, if Tensor is 2-D, return a 1-D Tensor containing the diagonal.
- Raises
ValueError – If the input tensor has less than two dimensions.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.trace()
: Return the sum along diagonals of the tensor.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> a = Tensor(np.arange(4).reshape(2, 2)) >>> print(a) [[0 1] [2 3]] >>> output = a.diagonal() >>> print(output) [0 3]
- property dtype
Return the dtype of the tensor (
mindspore.dtype
).
- expand_as(x)[source]
Expand the dimension of target tensor to the dimension of input tensor.
- Parameters
x (Tensor) – The input tensor. The shape of the input tensor must obey the broadcasting rule.
- Returns
Tensor, has the same dimension as input tensor.
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> from mindspore import dtype as mstype >>> x = Tensor([1, 2, 3], dtype=mstype.float32) >>> y = Tensor(np.ones((2, 3)), dtype=mstype.float32) >>> output = x.expand_as(y) >>> print(output) [[1. 2. 3.] [1. 2. 3.]]
- expand_dims(axis)[source]
Insert a dimension of shape 1 at the specified axis of Tensor
- Parameters
axis (int) – the axis at which to insert the singleton dimension.
- Returns
Tensor, with inserted dimension of length 1.
- Raises
TypeError – If axis is not an int.
ValueError – If axis is not in range [-self.ndim - 1, self.ndim + 1).
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.ones((2,2), dtype=np.float32)) >>> print(x) [[1. 1.] [1. 1.]] >>> print(x.shape) (2, 2) >>> y = x.expand_dims(axis=0) >>> print(y) [[[1. 1.] [1. 1.]]] >>> print(y.shape) (1, 2, 2)
- fill(value)[source]
Fill the tensor with a scalar value.
Note
Unlike Numpy, tensor.fill() will always return a new tensor, instead of filling the original tensor.
- Parameters
value (Union[None, int, float, bool]) – All elements of a will be assigned this value.
- Returns
Tensor, with the original dtype and shape.
- Raises
TypeError – If input arguments have types not specified above.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> a = Tensor(np.arange(4).reshape((2,2)).astype('float32')) >>> print(a.fill(1.0)) [[1. 1.] [1. 1.]]
- flatten(order='C')[source]
Return a copy of the tensor collapsed into one dimension.
- Parameters
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. Default: ‘C’.
- Returns
Tensor, has the same data type as input.
- Supported Platforms:
Ascend
GPU
CPU
- Raises
TypeError – If order is not string type.
ValueError – If order is string type, but not ‘C’ or ‘F’.
See also
mindspore.Tensor.reshape()
: Give a new shape to a tensor without changing its data.mindspore.Tensor.ravel()
: Return a contiguous flattened tensor.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.ones((2,3,4), dtype=np.float32)) >>> output = x.flatten() >>> print(output.shape) (24,)
- flush_from_cache()[source]
Flush cache data to host if tensor is cache enable.
Examples
>>> from mindspore import Tensor >>> import numpy as np >>> x = Tensor(np.array([1, 2], dtype=np.float32)) >>> y = x.flush_from_cache() >>> print(y) None
- static from_numpy(array)[source]
Convert numpy array to Tensor without copy data.
- Parameters
array (numpy.array) – The input array.
- Returns
Tensor, has the same data type as input array.
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = np.array([1, 2]) >>> output = Tensor.from_numpy(x) >>> print(output) [1 2]
- property has_init
Whether tensor is initialized.
- init_data(slice_index=None, shape=None, opt_shard_group=None)[source]
Get the tensor format data of this Tensor. The init_data function can be called once for the same tensor.
- Parameters
slice_index (int) – Slice index of a parameter’s slices. It is used when initialize a slice of a parameter, it guarantees that devices using the same slice can generate the same tensor. Default: None.
shape (list[int]) – Shape of the slice, it is used when initialize a slice of the parameter. Default: None.
opt_shard_group (str) – Optimizer shard group which is used in auto or semi auto parallel mode to get one shard of a parameter’s slice. Default: None.
- Returns
Initialized Tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore as ms >>> import mindspore.common.initializer as init >>> x = init.initializer(init.Constant(1), [2, 2], ms.float32) >>> out = x.init_data() >>> print(out) [[1. 1.] [1. 1.]]
- item(index=None)[source]
Get the item at the specified index of the tensor.
Note
Tensor.item returns a Tensor scalar instead of a Python scalar.
- Parameters
index (Union[None, int, tuple(int)]) – The index in Tensor. Default: None.
- Returns
A Tensor scalar, dtype is the same with the original Tensor.
- Raises
ValueError – If the length of the index is not equal to self.ndim.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.array([[1,2,3],[4,5,6]], dtype=np.float32)) >>> x = x.item((0,1)) >>> print(x) 2.0
- itemset(*args)[source]
Insert scalar into a tensor (scalar is cast to tensor’s dtype, if possible).
There must be at least 1 argument, and define the last argument as item. Then, tensor.itemset(*args) is equivalent to \(tensor[args] = item\).
- Parameters
args (Union[(numbers.Number), (int/tuple(int), numbers.Number)]) – The arguments that specify the index and value. If args contain one argument (a scalar), it is only used in case tensor is of size 1. If args contain two arguments, the last argument is the value to be set and must be a scalar, the first argument specifies a single tensor element location. It is either an int or a tuple.
- Returns
A new tensor that doesn’t affect the original tensor, with value set by \(tensor[args] = item\).
- Raises
ValueError – If the length of the first argument is not equal to self.ndim.
IndexError – If only one argument is provided, and the original Tensor is not scalar.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.array([[1,2,3],[4,5,6]], dtype=np.float32)) >>> print(x.itemset((0,1), 4)) [[1. 4. 3.] [4. 5. 6.]] >>> print(x) [[1. 2. 3.] [4. 5. 6.]]
- property itemsize
Return the length of one tensor element in bytes.
- masked_fill(mask, value)[source]
Fills elements of self tensor with value where mask is True. The shape of mask must be equal to the shape of the underlying tensor.
- Parameters
- Returns
Tensor, has the same type and shape as self.
- Raises
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> a = Tensor(np.arange(4)).astype('float32')) >>> print(a) [0. 1. 2. 3.] >>> mask = Tensor([False, False, True, True]) >>> print(a.masked_fill(mask, 0.0)) [0. 1. 0. 0.]
- max(axis=None, keepdims=False, initial=None, where=True)[source]
Return the maximum of a tensor or maximum along an axis.
- Parameters
axis (Union[None, int, tuple of ints], optional) – Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before. Default: None.
keepdims (bool, optional) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. Default: False.
initial (scalar, optional) – The minimum value of an output element. Must be present to allow computation on empty slice. Default: None.
where (bool Tensor, optional) – A boolean tensor which is broadcasted to match the dimensions of array, and selects elements to include in the reduction. If non-default value is passed, initial must also be provided. Default: True.
- Returns
Tensor or scalar, maximum of input tensor. If axis is None, the result is a scalar value. If axis is given, the result is a tensor of dimension
self.ndim - 1
.- Raises
TypeError – If arguments have types not specified above.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.argmin()
: Return the indices of the minimum values along an axis.mindspore.Tensor.argmax()
: Return the indices of the maximum values along an axis.mindspore.Tensor.min()
: Return the minimum of a tensor or minimum along an axis.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> a = Tensor(np.arange(4).reshape((2, 2)).astype('float32')) >>> output = a.max() >>> print(output) 3.0
- mean(axis=(), keep_dims=False)[source]
Reduce a dimension of a tensor by averaging all elements in the dimension.
- Parameters
axis (Union[None, int, tuple(int), list(int)]) – Dimensions of reduction. When the axis is None or empty tuple, reduce all dimensions. When the axis is int, tuple(int) or list(int), if the dimension of Tensor is dim, the value range is [-dim, dim). Default: ().
keep_dims (bool) – Whether to keep the reduced dimensions. Default: False.
- Returns
Tensor, has the same data type as input tensor.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.std()
: Compute the standard deviation along the specified axis.mindspore.Tensor.var()
: Compute the variance along the specified axis.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> input_x = Tensor(np.array([1, 2, 3], dtype=np.float32)) >>> output = input_x.mean() >>> print(output) 2.0
- min(axis=None, keepdims=False, initial=None, where=True)[source]
Return the minimum of a tensor or minimum along an axis.
- Parameters
axis (Union[None, int, tuple of ints], optional) – Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before. Default: None.
keepdims (bool, optional) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input tensor. Default: False.
initial (scalar, optional) – The maximum value of an output element. Must be present to allow computation on empty slice. Default: None.
where (bool Tensor, optional) – A boolean tensor which is broadcasted to match the dimensions of tensor, and selects elements to include in the reduction. If non-default value is passed, initial must also be provided. Default: True.
- Returns
Tensor or scalar, minimum of input tensor. If the axis is None, the result is a scalar value. If axis is given, the result is a tensor of dimension
self.ndim - 1
.- Raises
TypeError – If arguments have types not specified above.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.argmin()
: Return the indices of the minimum values along an axis.mindspore.Tensor.argmax()
: Return the indices of the maximum values along an axis.mindspore.Tensor.max()
: Return the maximum of a tensor or maximum along an axis.Examples
>>> from mindspore import Tensor >>> import mindspore.numpy as np >>> a = Tensor(np.arange(4).reshape((2,2)).astype('float32')) >>> output = a.min() >>> print(output) 0.0
- narrow(axis, start, length)[source]
Returns a narrowed tensor from input tensor. The dimension axis is input from start to start + length.
- Parameters
- Returns
Tensor.
output (Tensors) - The narrowed tensor.
- Raises
TypeError – If the input is not a tensor or tuple or list of tensors.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> from mindspore import Tensor >>> x = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], mindspore.int32) >>> output = x.narrow(0, 0, 2) >>> print(output) [[ 1 2 3] [ 4 5 6]] >>> output = x.narrow(1, 1, 2) >>> print(output) [[ 2 3] [ 5 6] [ 8 9]]
- property nbytes
Return the total number of bytes taken by the tensor.
- property ndim
Return the number of tensor dimensions.
- ptp(axis=None, keepdims=False)[source]
The name of the function comes from the acronym for “peak to peak”.
Note
Numpy argument out is not supported.
- Parameters
axis (Union[None, int, tuple(int)]) – Axis or axes along which the range is computed. The default is to compute the variance of the flattened tensor. Default: None.
keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the tensor. Default is False.
- Returns
Tensor.
- Raises
TypeError – If self is not a tensor, or axis and keepdims have types not specified above.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> from mindspore import Tensor >>> x = Tensor([[4.0, 9.0, 2.0, 10.0], [6.0, 9.0, 7.0, 12.0]]).astype("float32") >>> print(x.ptp(axis=1)) [8. 6.] >>> print(x.ptp(axis=0)) [2. 0. 5. 2.]
- ravel()[source]
Return a contiguous flattened tensor.
- Returns
Tensor, a 1-D tensor, containing the same elements of the input.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.reshape()
: Give a new shape to a tensor without changing its data.mindspore.Tensor.flatten()
: Return a copy of the tensor collapsed into one dimension.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.ones((2,3,4), dtype=np.float32)) >>> output = x.ravel() >>> print(output.shape) (24,)
- repeat(repeats, axis=None)[source]
Repeat elements of a tensor.
- Parameters
- Returns
Tensor, has the same shape as input tensor except along the given axis.
- Raises
ValueError – If the axis is out of range.
TypeError – If arguments have types not specified above.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.reshape()
: Give a new shape to a tensor without changing its data.mindspore.Tensor.resize()
: Changes shape and size of tensor in-place.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.array(3)) >>> print(x.repeat(4)) [3 3 3 3] >>> x = Tensor(np.array([[1, 2],[3, 4]])) >>> print(x.repeat(2)) [1 1 2 2 3 3 4 4] >>> print(x.repeat(3, axis=1)) [[1 1 1 2 2 2] [3 3 3 4 4 4]] >>> print(x.repeat([1,2], axis=0)) [[1 2] [3 4] [3 4]]
- reshape(*shape)[source]
Give a new shape to a tensor without changing its data.
- Parameters
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 tensor of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the tensor and remaining dimensions.
- Returns
Tensor, with new specified shape.
- Raises
TypeError – If new shape is not integer, list or tuple.
ValueError – If new shape is not compatible with the original shape.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> from mindspore import Tensor >>> from mindspore import dtype as mstype >>> x = Tensor([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]], dtype=mstype.float32) >>> output = x.reshape((3, 2)) >>> print(output) [[-0.1 0.3] [ 3.6 0.4] [ 0.5 -3.2]]
- resize(*new_shape)[source]
Changes shape and size of tensor in-place.
If the shape of the new tensor is larger than the shape of the original tensor, the new tensor will be filled with 0. And if the shape of the new tensor is smaller than the shape of the original tensor, the new tensor is filled with the elements of the original tensor in order.
Note
Instead of changing the size of the input tensor and returns nothing as in numpy, this method returns a new Tensor with the input size. Numpy argument refcheck is not supported.
- Parameters
new_shape (Union[ints, tuple of ints]) – Shape of resized tensor.
- Returns
Tensor.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.reshape()
: Give a new shape to a tensor without changing its data.mindspore.Tensor.repeat()
: Repeat elements of a tensor.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)) >>> y = x.resize(3, 3) >>> print(y) [[1. 2. 3.] [4. 5. 6.] [0. 0. 0.]] >>> y = x.resize(2, 2) >>> print(y) [[1. 2.] [3. 4.]]
- searchsorted(v, side='left', sorter=None)[source]
Finds indices where elements should be inserted to maintain order.
- Parameters
v (Union[int, float, bool, list, tuple, Tensor]) – Values to insert into the tensor.
side ('left', 'right', optional) – If ‘left’, the index of the first suitable location found is given. If ‘right’, return the last such index. If there is no suitable index, return either 0 or N (where N is the length of the tensor). Default: ‘left’.
sorter (Union[int, float, bool, list, tuple, Tensor]) – 1-D optional tensor of integer indices that sort the tensor into ascending order. They are typically the result of argsort. Default: None.
- Returns
Tensor, array of insertion points with the same shape as v.
- Raises
ValueError – If argument for side or sorter is invalid.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.array([1, 2, 3, 4, 5])) >>> print(x.searchsorted(3)) 2
- property shape
Returns the shape of the tensor as a tuple.
- property size
Returns the total number of elements in tensor.
- squeeze(axis=None)[source]
Remove the dimension of shape 1 from the Tensor
- Parameters
axis (Union[None, int, list(int), tuple(int)], optional) – Selects a subset of the entries of length one in the shape. If an axis is selected with shape entry greater than one, an error is raised. Default is None.
- Returns
Tensor, with all or a subset of the dimensions of length 1 removed.
- Raises
TypeError – If input arguments have types not specified above.
ValueError – If axis is greater than one.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.expand_as()
: Expand the dimension of target tensor to the dimension of input tensor.mindspore.Tensor.reshape()
: Give a new shape to a tensor without changing its data.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.ones((1,2,2), dtype=np.float32)) >>> print(x) [[[1. 1.] [1. 1.]]] >>> print(x.shape) (1, 2, 2) >>> y = x.squeeze() >>> print(y) [[1. 1.] [1. 1.]] >>> print(y.shape) (2, 2) >>> y = x.squeeze(axis=0) >>> print(y) [[1. 1.] [1. 1.]] >>> print(y.shape) (2, 2)
- std(axis=None, ddof=0, keepdims=False)[source]
Compute the standard deviation along the specified axis.
The standard deviation is the square root of the average of the squared deviations from the mean, i.e., \(std = sqrt(mean(abs(x - x.mean())**2))\).
Return the standard deviation, which is computed for the flattened array by default, otherwise over the specified axis.
Note
Numpy arguments dtype, out and where are not supported.
- Parameters
axis (Union[None, int, tuple(int)]) –
Axis or axes along which the standard deviation is computed. Default: None.
If None, compute the standard deviation of the flattened array.
ddof (int) – Means Delta Degrees of Freedom. The divisor used in calculations is \(N - ddof\), where \(N\) represents the number of elements. Default: 0.
keepdims – Default: False.
- Returns
Standard deviation tensor.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.mean()
: Reduce a dimension of a tensor by averaging all elements in the dimension.mindspore.Tensor.var()
: Compute the variance along the specified axis.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> input_x = Tensor(np.array([1, 2, 3, 4], dtype=np.float32)) >>> output = input_x.std() >>> print(output) 1.118034
- property strides
Return the tuple of bytes to step in each dimension when traversing a tensor.
- sum(axis=None, dtype=None, keepdims=False, initial=None)[source]
Return sum of tensor elements over a given axis.
Note
Numpy arguments out, where, casting, order, subok, signature, and extobj are not supported.
- Parameters
axis (Union[None, int, tuple(int)]) – Axis or axes along which a sum is performed. Default: None. If None, sum all the elements of the input tensor. If the axis is negative, it counts from the last to the first axis. If the axis is a tuple of ints, a sum is performed on all the axes specified in the tuple instead of a single axis or all the axes as before.
dtype (
mindspore.dtype
, optional) – defaults to None. Overrides the dtype of the output Tensor.keepdims (bool) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then keepdims will not be passed through to the sum method of sub-classes of ndarray, however any non-default value will be. If the sub-class method does not implement keepdims any exceptions will be raised. Default: False.
initial (scalar) – Starting value for the sum. Default: None.
- Returns
Tensor. A tensor with the same shape as input, with the specified axis removed. If the input tensor is a 0-d array, or if the axis is None, a scalar is returned.
- Raises
TypeError – If input is not array_like, or axis is not int or tuple of ints, or keepdims is not integer, or initial is not scalar.
ValueError – If any axis is out of range or duplicate axes exist.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.cumsum()
: Return the cumulative sum of the elements along a given axis.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> input_x = Tensor(np.array([-1, 0, 1]).astype(np.float32)) >>> print(input_x.sum()) 0.0 >>> input_x = Tensor(np.arange(10).reshape(2, 5).astype(np.float32)) >>> print(input_x.sum(axis=1)) [10. 35.]
- swapaxes(axis1, axis2)[source]
Interchange two axes of a tensor.
- Parameters
- Returns
Transposed tensor, has the same data type as the input.
- Raises
TypeError – If axis1 or axis2 is not integer.
ValueError – If axis1 or axis2 is not in the range of \([-ndim, ndim-1]\).
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.ones((2,3,4), dtype=np.float32)) >>> output = x.swapaxes(0, 2) >>> print(output.shape) (4,3,2)
- take(indices, axis=None, mode='clip')[source]
Takes elements from a tensor along an axis.
- Parameters
indices (Tensor) – The indices with shape (Nj…) of the values to extract.
axis (int, optional) – The axis over which to select values. By default, the flattened input tensor is used. Default: None.
mode ('raise', 'wrap', 'clip', optional) –
Default: “clip”.
’raise’ – Raises an error;
’wrap’ – Wraps around;
’clip’ – Clips to the range. ‘clip’ mode means that all indices that are too large are replaced by the index that addresses the last element along that axis. Note that this disables indexing with negative numbers.
Default: ‘clip’.
- Returns
Tensor, the indexed result.
- Raises
ValueError – If axis is out of range, or mode has values other than (‘raise’, ‘wrap’, ‘clip’)
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> a = Tensor(np.array([4, 3, 5, 7, 6, 8])) >>> indices = Tensor(np.array([0, 1, 4])) >>> output = a.take(indices) >>> print(output) [4 3 6]
- to_tensor(slice_index=None, shape=None, opt_shard_group=None)[source]
Return init_data() and get the tensor format data of this Tensor.
Note
The usage of to_tensor is deprecated. Please use init_data.
- Parameters
slice_index (int) – Slice index of a parameter’s slices. It is used when initialize a slice of a parameter, it guarantees that devices using the same slice can generate the same tensor. Default: None.
shape (list[int]) – Shape of the slice, it is used when initialize a slice of the parameter. Default: None.
opt_shard_group (str) – Optimizer shard group which is used in auto or semi auto parallel mode to get one shard of a parameter’s slice. Default: None.
- Returns
Initialized Tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore as ms >>> import mindspore.common.initializer as init >>> x = init.initializer(init.Constant(1), [2, 2], ms.float32) >>> out = x.to_tensor() >>> print(out) [[1. 1.] [1. 1.]]
- trace(offset=0, axis1=0, axis2=1, dtype=None)[source]
Return the sum along diagonals of the tensor.
- Parameters
offset (int, optional) – Offset of the diagonal from the main diagonal. Can be positive or negative. Defaults to main diagonal.
axis1 (int, optional) – Axis to be used as the first axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults to first axis (0).
axis2 (int, optional) – Axis to be used as the second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults to second axis.
dtype (
mindspore.dtype
, optional) – defaults to None. Overrides the dtype of the output Tensor.
- Returns
Tensor, the sum along diagonals.
- Raises
ValueError – If the input tensor has less than two dimensions.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.diagonal()
: Return specified diagonals.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.eye(3, dtype=np.float32)) >>> print(x.trace()) 3.0
- transpose(*axes)[source]
Return a tensor with axes transposed.
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 an 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])
, thentensor.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])
.- Parameters
axes (Union[None, tuple(int), list(int), int], optional) – If axes is None or blank, the method 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.
- Raises
TypeError – If input arguments have types not specified above.
ValueError – If the number of axes is not equal to Tensor’s ndim.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> x = Tensor(np.ones((1,2,3), dtype=np.float32)) >>> x = x.transpose() >>> print(x.shape) (3, 2, 1)
- var(axis=None, ddof=0, keepdims=False)[source]
Compute the variance along the specified axis.
The variance is the average of the squared deviations from the mean, i.e., \(var = mean(abs(x - x.mean())**2)\).
Return the variance, which is computed for the flattened array by default, otherwise over the specified axis.
Note
Numpy arguments dtype, out and where are not supported.
- Parameters
axis (Union[None, int, tuple(int)]) – Axis or axes along which the variance is computed. The default is to compute the variance of the flattened array. Default: None.
ddof (int) – Means Delta Degrees of Freedom. Default: 0. The divisor used in calculations is \(N - ddof\), where \(N\) represents the number of elements.
keepdims (bool) – Default: False.
- Returns
Variance tensor.
- Supported Platforms:
Ascend
GPU
CPU
See also
mindspore.Tensor.mean()
: Reduce a dimension of a tensor by averaging all elements in the dimension.mindspore.Tensor.std()
: Compute the standard deviation along the specified axis.Examples
>>> import numpy as np >>> from mindspore import Tensor >>> input_x = Tensor(np.array([1., 2., 3., 4.], np.float32)) >>> output = input_x.var() >>> print(output) 1.25
- view(*shape)[source]
Reshape the tensor according to the input shape.
- Parameters
shape (Union[tuple(int), int]) – Dimension of the output tensor.
- Returns
Tensor, has the same dimension as the input shape.
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.]]