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.
- 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. If None in shape, a tensor of dynamic shape is created, input_data doesn’t need to be set; if None not 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. 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.
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.
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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Construct a tensor from an index tensor and a list of tensors to choose from. |
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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 new tensor containing imaginary value of the input tensor. |
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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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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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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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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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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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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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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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Return the sum along diagonals of the tensor. |
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Alias for Tensor.div() with \(rounding\_mode=None\). |
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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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