Tensor

Tensor is a basic data structure in the MindSpore network computing.

Import the required modules and APIs:

import numpy as np
from mindspore import Tensor, context
from mindspore import dtype as mstype
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")

Initializing a Tensor

There are multiple methods for initializing tensors. When building a tensor, you can pass the Tensor, float, int, bool, tuple, list, and NumPy.array types.

Generate a tensor based on data.

You can create a tensor based on data. The data type can be set or automatically inferred.

x = Tensor(0.1)

Generate a tensor from the NumPy array.

You can create a tensor from the NumPy array.

arr = np.array([1, 0, 1, 0])
x_np = Tensor(arr)

If the initial value is NumPy.array, the generated Tensor data type corresponds to NumPy.array.

Inherit attributes of another tensor to form a new tensor.

from mindspore import ops
oneslike = ops.OnesLike()
x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32))
output = oneslike(x)
print(output)

    [[1 1]
     [1 1]]

Output a constant tensor of a specified size.

shape is the size tuple of a tensor, which determines the dimension of the output tensor.

from mindspore.ops import operations as ops

shape = (2, 2)
ones = ops.Ones()
output = ones(shape, mstype.float32)
print(output)

zeros = ops.Zeros()
output = zeros(shape, mstype.float32)
print(output)

    [[1. 1.]
     [1. 1.]]
    [[0. 0.]
     [0. 0.]]

During Tensor initialization, dtype can be specified to, for example, mstype.int32, mstype.float32 or mstype.bool_.

Tensor Attributes

Tensor attributes include shape and data type (dtype).

shape: a tuple
dtype: a data type of MindSpore

t1 = Tensor(np.zeros([1,2,3]), mstype.float32)
print("Datatype of tensor: {}".format(t1.dtype))
print("Shape of tensor: {}".format(t1.shape))

    Datatype of tensor: Float32
    Shape of tensor: (1, 2, 3)

Tensor Operation

There are many operations between tensors, including arithmetic, linear algebra, matrix processing (transposing, indexing, and slicing), and sampling. The following describes several operations. The usage of tensor computation is similar to that of NumPy.

Indexing and slicing operations similar to NumPy:

tensor = Tensor(np.array([[0, 1], [2, 3]]).astype(np.float32))
print("First row: {}".format(tensor[0]))
print("First column: {}".format(tensor[:, 0]))
print("Last column: {}".format(tensor[..., -1]))

    First row: [0. 1.]
    First column: [0. 2.]
    Last column: [1. 3.]

Concat connects a series of tensors in a given dimension.

data1 = Tensor(np.array([[0, 1], [2, 3]]).astype(np.float32))
data2 = Tensor(np.array([[4, 5], [6, 7]]).astype(np.float32))
op = ops.Concat()
output = op((data1, data2))
print(output)

    [[0. 1.]
     [2. 3.]
     [4. 5.]
     [6. 7.]]

Stack combines two tensors from another dimension.

data1 = Tensor(np.array([[0, 1], [2, 3]]).astype(np.float32))
data2 = Tensor(np.array([[4, 5], [6, 7]]).astype(np.float32))
op = ops.Stack()
output = op([data1, data2])
print(output)

    [[[0. 1.]
      [2. 3.]]

     [[4. 5.]
      [6. 7.]]]

Common computation:

input_x = Tensor(np.array([1.0, 2.0, 3.0]), mstype.float32)
input_y = Tensor(np.array([4.0, 5.0, 6.0]), mstype.float32)
mul = ops.Mul()
output = mul(input_x, input_y)
print(output)

    [ 4. 10. 18.]

Conversion Between Tensor and NumPy

Tensor and NumPy can be converted to each other.

Tensor to NumPy

zeros = ops.Zeros()
output = zeros((2,2), mstype.float32)
print("output: {}".format(type(output)))
n_output = output.asnumpy()
print("n_output: {}".format(type(n_output)))

    output: <class 'mindspore.common.tensor.Tensor'>
    n_output: <class 'numpy.ndarray'>

NumPy to Tensor

output = np.array([1, 0, 1, 0])
print("output: {}".format(type(output)))
t_output = Tensor(output)
print("t_output: {}".format(type(t_output)))

    output: <class 'numpy.ndarray'>
    t_output: <class 'mindspore.common.tensor.Tensor'>