mindspore.Tensor
- class mindspore.Tensor(input_data=None, dtype=None, shape=None, init=None, internal=False, const_arg=False)[source]
Tensor is a data structure that stores an n-dimensional array.
Note
If init interface is used to initialize Tensor, the Tensor.init_data API needs to be called to load the actual data to Tensor.
Warning
To convert dtype of a Tensor, it is recommended to use Tensor.astype() rather than Tensor(sourceTensor, dtype=newDtype).
- 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 isNone
, the data type of the output Tensor will be the same as the input_data. Default:None
.shape (Union[tuple, list, int,
mindspore.Symbol
]) – Used to indicate the shape of the output Tensor. If input_data is available, shape doesn't need to be set. IfNone
or Symbol exists in shape , a tensor of dynamic shape is created, input_data doesn't need to be set; if only integers exist in shape, a tensor of static shape is created, input_data or init must be set. Default:None
.init (Initializer) – The information of init data. init is used for delayed initialization in parallel mode, when using init, dtype and shape must be set. Default:
None
.internal (bool) – Whether it is created by the framework.
'True'
means that the tensor is created by framework.'False'
means that the tensor is created by user. Default:False
.const_arg (bool) – Whether the tensor is a constant when it is used for the argument of a network. Default:
False
.
- Outputs:
Tensor.
Note
The default value
None
of input_data works as a placeholder, it does not mean that we can create a NoneType Tensor. Tensor with shape contains 0 is not fully tested and supported.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
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Returns the maximum value with corresponding index. |
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Returns the minimum value with corresponding index. |
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Convert tensor to numpy array. |
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Assign another tensor value to this tensor. |
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Return a copy of the tensor, cast to a specified type. |
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Converts input tensor dtype to bool. |
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Fills the tensor with numbers drawn from the Cauchy distribution. |
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Construct a tensor from an index tensor and a list of tensors to choose from. |
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Converts a Tensor into a continuous-memory Tensor that contains the same data as the original Tensor. |
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Return a copy of the tensor. |
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Return the dtype of the tensor ( |
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Expand the dimension of target tensor to the dimension of input tensor. |
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Fills the main diagonal of a Tensor with a specified value and returns the result. |
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Converts input tensor dtype to float32. |
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Flush cache data to host if tensor is cache enable. |
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Convert numpy array to Tensor. |
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Returns a view of a matrix (2-D tensor) conjugated and transposed. |
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Converts input tensor dtype to float16. |
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Whether tensor is initialized. |
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Returns a Tensor. |
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Get the tensor format data of this Tensor. |
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Converts input tensor dtype to int32. |
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Judge whether the data type of tensor is a signed data type. |
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Determines whether the memory of tensor is contiguous. |
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Get the item at the specified index of the tensor. |
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Insert scalar into a tensor (scalar is cast to tensor's dtype, if possible). |
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Return the length of one tensor element in bytes. |
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Fills the elements of the input tensor with log normal values initialized by given mean and std: |
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Converts input tensor dtype to int64. |
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Returns a Tensor. |
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Return the maximum of a tensor or maximum along an axis. |
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Accessing this property is equivalent to Calling self.adjoint(). |
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Return the minimum of a tensor or minimum along an axis. |
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Copy Tensor to target device synchronously or asynchronously, default synchronously. |
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Returns the Tensor that exchanges the last two dimensions. |
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Return the total number of bytes taken by the tensor. |
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Return the number of tensor dimensions. |
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Return a tensor of size filled with ones. |
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Return a tensor of size filled with zeros. |
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The name of the function comes from the acronym for "peak to peak". |
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Return a contiguous flattened tensor. |
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Repeat elements of a tensor. |
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Rearranges the input Tensor based on the given shape . |
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Change the shape of the Tensor to the shape of other without changing the data. |
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Changes shape and size of tensor in-place. |
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Registers a backward hook for tensor. |
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Creates a new tensor by subtracting the values from the positions in self tensor indicated by indices, with values from updates. |
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Finds indices where elements should be inserted to maintain order. |
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Specify whether the tensor is a constant when it is used for the argument of a network. |
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Return a copy of the tensor, cast to int16 type, equivalent to self.astype(mstype.int16). |
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Tensor's offset in the underlying storage in terms of the number of storage elements. |
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The stride to jump from one element to the next in the input dim. |
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Return the tuple of bytes to step in each dimension when traversing a tensor. |
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Return sum of tensor elements over a given axis. |
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Sum self Tensor to the size. |
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Return the transposed tensor. |
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Takes elements from a tensor along an axis. |
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Performs tensor dtype conversion. |
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Convert a Tensor to COOTensor. |
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Convert a Tensor to CSRTensor. |
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Convert a Tensor to List. |
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Return the sum along diagonals of the tensor. |
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Alias for Tensor.div() with \(rounding\_mode=None\). |
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Change the dtype of the Tensor to the dtype . |
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Change the dtype of the Tensor to the dtype of other. |
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Generates random numbers in the half-open interval [from_, to). |
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Compute the variance along the specified axis. |
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Reshape the tensor according to the input shape. |
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View self Tensor as the same shape as other . |
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