mindspore.numpy

MindSpore NumPy工具包提供了一系列类NumPy接口。用户可以使用类NumPy语法在MindSpore上进行模型的搭建。

MindSpore Numpy具有四大功能模块:Array生成、Array操作、逻辑运算和数学运算。

在API示例中,常用的模块导入方法如下:

import mindspore.numpy as np

Note

MindSpore numpy通过组装底层算子来提供与numpy一致的编程体验接口,方便开发人员使用和代码移植。相比于MindSpore的functional和ops接口,与原始numpy的接口格式及行为一致性更好,以便于用户理解和使用。注意:由于兼容numpy的考虑,部分接口的性能可能弱于functional和ops接口。使用者可以按需选择不同类型的接口。

Array生成

生成类算子用来生成和构建具有指定数值、类型和形状的数组(Tensor)。

构建数组代码示例:

import mindspore.numpy as np
import mindspore.ops as ops
input_x = np.array([1, 2, 3], np.float32)
print("input_x =", input_x)
print("type of input_x =", ops.typeof(input_x))

运行结果如下:

input_x = [1. 2. 3.]
type of input_x = Tensor[Float32]

除了使用上述方法来创建外,也可以通过以下几种方式创建。

  • 生成具有相同元素的数组

    生成具有相同元素的数组代码示例:

    input_x = np.full((2, 3), 6, np.float32)
    print(input_x)
    

    运行结果如下:

    [[6. 6. 6.]
     [6. 6. 6.]]
    

    生成指定形状的全1数组,示例:

    input_x = np.ones((2, 3), np.float32)
    print(input_x)
    

    运行结果如下:

    [[1. 1. 1.]
     [1. 1. 1.]]
    
  • 生成具有某个范围内的数值的数组

    生成指定范围内的等差数组代码示例:

    input_x = np.arange(0, 5, 1)
    print(input_x)
    

    运行结果如下:

    [0 1 2 3 4]
    
  • 生成特殊类型的数组

    生成给定对角线处下方元素为1,上方元素为0的矩阵,示例:

    input_x = np.tri(3, 3, 1)
    print(input_x)
    

    运行结果如下:

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

    生成对角线为1,其他元素为0的二维矩阵,示例:

    input_x = np.eye(2, 2)
    print(input_x)
    

    运行结果如下:

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

API Name

Description

Supported Platforms

mindspore.numpy.arange

Returns evenly spaced values within a given interval.

Ascend GPU CPU

mindspore.numpy.array

Creates a tensor.

Ascend GPU CPU

mindspore.numpy.asarray

Converts the input to tensor.

Ascend GPU CPU

mindspore.numpy.asfarray

Similar to asarray, converts the input to a float tensor.

Ascend GPU CPU

mindspore.numpy.bartlett

Returns the Bartlett window.

Ascend GPU CPU

mindspore.numpy.blackman

Returns the Blackman window.

Ascend GPU CPU

mindspore.numpy.copy

Returns a tensor copy of the given object.

Ascend GPU CPU

mindspore.numpy.diag

Extracts a diagonal or construct a diagonal array.

Ascend GPU CPU

mindspore.numpy.diag_indices

Returns the indices to access the main diagonal of an array.

Ascend GPU CPU

mindspore.numpy.diagflat

Creates a two-dimensional array with the flattened input as a diagonal.

Ascend GPU CPU

mindspore.numpy.diagonal

Returns specified diagonals.

Ascend GPU CPU

mindspore.numpy.empty

Returns a new array of given shape and type, without initializing entries.

Ascend GPU CPU

mindspore.numpy.empty_like

Returns a new array with the same shape and type as a given array.

Ascend GPU CPU

mindspore.numpy.eye

Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.

Ascend GPU CPU

mindspore.numpy.full

Returns a new tensor of given shape and type, filled with fill_value.

Ascend GPU CPU

mindspore.numpy.full_like

Returns a full array with the same shape and type as a given array.

Ascend GPU CPU

mindspore.numpy.geomspace

Returns numbers spaced evenly on a log scale (a geometric progression).

Ascend GPU CPU

mindspore.numpy.hamming

Returns the Hamming window.

Ascend GPU CPU

mindspore.numpy.hanning

Returns the Hanning window.

Ascend GPU CPU

mindspore.numpy.histogram_bin_edges

Function to calculate only the edges of the bins used by the histogram function.

Ascend GPU CPU

mindspore.numpy.identity

Returns the identity tensor.

Ascend GPU CPU

mindspore.numpy.indices

Returns an array representing the indices of a grid.

Ascend GPU CPU

mindspore.numpy.ix_

Constructs an open mesh from multiple sequences.

Ascend GPU CPU

mindspore.numpy.linspace

Returns evenly spaced values within a given interval.

Ascend GPU CPU

mindspore.numpy.logspace

Returns numbers spaced evenly on a log scale.

Ascend GPU CPU

mindspore.numpy.meshgrid

Returns coordinate matrices from coordinate vectors.

Ascend GPU CPU

mindspore.numpy.mgrid

mgrid is an NdGrid instance with sparse=False.

Ascend GPU CPU

mindspore.numpy.ogrid

ogrid is an NdGrid instance with sparse=True.

Ascend GPU CPU

mindspore.numpy.ones

Returns a new tensor of given shape and type, filled with ones.

Ascend GPU CPU

mindspore.numpy.ones_like

Returns an array of ones with the same shape and type as a given array.

Ascend GPU CPU

mindspore.numpy.pad

Pads an array.

Ascend GPU CPU

mindspore.numpy.rand

Returns a new Tensor with given shape and dtype, filled with random numbers from the uniform distribution on the interval \([0, 1)\).

Ascend GPU CPU

mindspore.numpy.randint

Return random integers from minval (inclusive) to maxval (exclusive).

Ascend GPU CPU

mindspore.numpy.randn

Returns a new Tensor with given shape and dtype, filled with a sample (or samples) from the standard normal distribution.

Ascend GPU CPU

mindspore.numpy.trace

Returns the sum along diagonals of the array.

Ascend GPU CPU

mindspore.numpy.tri

Returns a tensor with ones at and below the given diagonal and zeros elsewhere.

Ascend GPU CPU

mindspore.numpy.tril

Returns a lower triangle of a tensor.

Ascend GPU CPU

mindspore.numpy.tril_indices

Returns the indices for the lower-triangle of an (n, m) array.

Ascend GPU CPU

mindspore.numpy.tril_indices_from

Returns the indices for the lower-triangle of arr.

Ascend GPU CPU

mindspore.numpy.triu

Returns an upper triangle of a tensor.

Ascend GPU CPU

mindspore.numpy.triu_indices

Returns the indices for the upper-triangle of an (n, m) array.

Ascend GPU CPU

mindspore.numpy.triu_indices_from

Returns the indices for the upper-triangle of arr.

Ascend GPU CPU

mindspore.numpy.vander

Generates a Vandermonde matrix.

Ascend GPU CPU

mindspore.numpy.zeros

Returns a new tensor of given shape and type, filled with zeros.

Ascend GPU CPU

mindspore.numpy.zeros_like

Returns an array of zeros with the same shape and type as a given array.

Ascend GPU CPU

Array操作

操作类算子主要进行数组的维度变换,分割和拼接等。

  • 数组维度变换

    矩阵转置,代码示例:

    input_x = np.arange(10).reshape(5, 2)
    output = np.transpose(input_x)
    print(output)
    

    运行结果如下:

    [[0 2 4 6 8]
     [1 3 5 7 9]]
    

    交换指定轴,代码示例:

    input_x = np.ones((1, 2, 3))
    output = np.swapaxes(input_x, 0, 1)
    print(output.shape)
    

    运行结果如下:

    (2, 1, 3)
    
  • 数组分割

    将输入数组平均切分为多个数组,代码示例:

    input_x = np.arange(9)
    output = np.split(input_x, 3)
    print(output)
    

    运行结果如下:

    (Tensor(shape=[3], dtype=Int32, value= [0, 1, 2]), Tensor(shape=[3], dtype=Int32, value= [3, 4, 5]), Tensor(shape=[3], dtype=Int32, value= [6, 7, 8]))
    
  • 数组拼接

    将两个数组按照指定轴进行拼接,代码示例:

    input_x = np.arange(0, 5)
    input_y = np.arange(10, 15)
    output = np.concatenate((input_x, input_y), axis=0)
    print(output)
    

    运行结果如下:

    [ 0  1  2  3  4 10 11 12 13 14]
    

API Name

Description

Supported Platforms

mindspore.numpy.append

Appends values to the end of a tensor.

Ascend GPU CPU

mindspore.numpy.apply_along_axis

Applies a function to 1-D slices along the given axis.

Ascend GPU CPU

mindspore.numpy.apply_over_axes

Applies a function repeatedly over multiple axes.

Ascend GPU CPU

mindspore.numpy.array_split

Splits a tensor into multiple sub-tensors.

Ascend GPU CPU

mindspore.numpy.array_str

Returns a string representation of the data in an array.

Ascend GPU CPU

mindspore.numpy.atleast_1d

Converts inputs to arrays with at least one dimension.

Ascend GPU CPU

mindspore.numpy.atleast_2d

Reshapes inputs as arrays with at least two dimensions.

Ascend GPU CPU

mindspore.numpy.atleast_3d

Reshapes inputs as arrays with at least three dimensions.

Ascend GPU CPU

mindspore.numpy.broadcast_arrays

Broadcasts any number of arrays against each other.

Ascend GPU CPU

mindspore.numpy.broadcast_to

Broadcasts an array to a new shape.

Ascend GPU CPU

mindspore.numpy.choose

Construct an array from an index array and a list of arrays to choose from.

Ascend GPU CPU

mindspore.numpy.column_stack

Stacks 1-D tensors as columns into a 2-D tensor.

Ascend GPU CPU

mindspore.numpy.concatenate

Joins a sequence of tensors along an existing axis.

Ascend GPU CPU

mindspore.numpy.dsplit

Splits a tensor into multiple sub-tensors along the 3rd axis (depth).

Ascend GPU CPU

mindspore.numpy.dstack

Stacks tensors in sequence depth wise (along the third axis).

Ascend GPU CPU

mindspore.numpy.expand_dims

Expands the shape of a tensor.

Ascend GPU CPU

mindspore.numpy.flip

Reverses the order of elements in an array along the given axis.

GPU CPU

mindspore.numpy.fliplr

Flips the entries in each row in the left/right direction.

GPU CPU

mindspore.numpy.flipud

Flips the entries in each column in the up/down direction.

GPU CPU

mindspore.numpy.hsplit

Splits a tensor into multiple sub-tensors horizontally (column-wise).

Ascend GPU CPU

mindspore.numpy.hstack

Stacks tensors in sequence horizontally.

Ascend GPU CPU

mindspore.numpy.moveaxis

Moves axes of an array to new positions.

Ascend GPU CPU

mindspore.numpy.piecewise

Evaluates a piecewise-defined function.

Ascend GPU CPU

mindspore.numpy.ravel

Returns a contiguous flattened tensor.

Ascend GPU CPU

mindspore.numpy.repeat

Repeats elements of an array.

Ascend GPU CPU

mindspore.numpy.reshape

Reshapes a tensor without changing its data.

Ascend GPU CPU

mindspore.numpy.roll

Rolls a tensor along given axes.

Ascend GPU CPU

mindspore.numpy.rollaxis

Rolls the specified axis backwards, until it lies in the given position.

Ascend GPU CPU

mindspore.numpy.rot90

Rotates a tensor by 90 degrees in the plane specified by axes.

GPU

mindspore.numpy.select

Returns an array drawn from elements in choicelist, depending on conditions.

Ascend GPU CPU

mindspore.numpy.size

Returns the number of elements along a given axis.

Ascend GPU CPU

mindspore.numpy.split

Splits a tensor into multiple sub-tensors along the given axis.

Ascend GPU CPU

mindspore.numpy.squeeze

Removes single-dimensional entries from the shape of a tensor.

Ascend GPU CPU

mindspore.numpy.stack

Joins a sequence of arrays along a new axis.

Ascend GPU CPU

mindspore.numpy.swapaxes

Interchanges two axes of a tensor.

Ascend GPU CPU

mindspore.numpy.take

Takes elements from an array along an axis.

Ascend GPU CPU

mindspore.numpy.take_along_axis

Takes values from the input array by matching 1d index and data slices.

Ascend GPU CPU

mindspore.numpy.tile

Constructs an array by repeating a the number of times given by reps.

Ascend GPU CPU

mindspore.numpy.transpose

Reverses or permutes the axes of a tensor; returns the modified tensor.

Ascend GPU CPU

mindspore.numpy.unique

Finds the unique elements of a tensor.

Ascend GPU CPU

mindspore.numpy.unravel_index

Converts a flat index or array of flat indices into a tuple of coordinate arrays.

Ascend GPU CPU

mindspore.numpy.vsplit

Splits a tensor into multiple sub-tensors vertically (row-wise).

Ascend GPU CPU

mindspore.numpy.vstack

Stacks tensors in sequence vertically.

Ascend GPU CPU

mindspore.numpy.where

Returns elements chosen from x or y depending on condition.

Ascend GPU CPU

逻辑运算

逻辑运算类算子主要进行各类逻辑相关的运算。

相等(equal)和小于(less)计算代码示例如下:

input_x = np.arange(0, 5)
input_y = np.arange(0, 10, 2)
output = np.equal(input_x, input_y)
print("output of equal:", output)
output = np.less(input_x, input_y)
print("output of less:", output)

运行结果如下:

output of equal: [ True False False False False]
output of less: [False  True  True  True  True]

API Name

Description

Supported Platforms

mindspore.numpy.array_equal

Returns True if input arrays have same shapes and all elements equal.

GPU CPU Ascend

mindspore.numpy.array_equiv

Returns True if input arrays are shape consistent and all elements equal.

Ascend GPU CPU

mindspore.numpy.equal

Returns the truth value of (x1 == x2) element-wise.

Ascend GPU CPU

mindspore.numpy.greater

Returns the truth value of (x1 > x2) element-wise.

Ascend GPU CPU

mindspore.numpy.greater_equal

Returns the truth value of (x1 >= x2) element-wise.

Ascend GPU CPU

mindspore.numpy.in1d

Tests whether each element of a 1-D array is also present in a second array.

Ascend GPU CPU

mindspore.numpy.isclose

Returns a boolean tensor where two tensors are element-wise equal within a tolerance.

Ascend GPU CPU

mindspore.numpy.isfinite

Tests element-wise for finiteness (not infinity or not Not a Number).

Ascend GPU CPU

mindspore.numpy.isin

Calculates element in test_elements, broadcasting over element only.

Ascend GPU CPU

mindspore.numpy.isinf

Tests element-wise for positive or negative infinity.

GPU CPU

mindspore.numpy.isnan

Tests element-wise for NaN and return result as a boolean array.

GPU CPU

mindspore.numpy.isneginf

Tests element-wise for negative infinity, returns result as bool array.

GPU CPU

mindspore.numpy.isposinf

Tests element-wise for positive infinity, returns result as bool array.

GPU CPU

mindspore.numpy.isscalar

Returns True if the type of element is a scalar type.

Ascend GPU CPU

mindspore.numpy.less

Returns the truth value of (x1 < x2) element-wise.

Ascend GPU CPU

mindspore.numpy.less_equal

Returns the truth value of (x1 <= x2) element-wise.

Ascend GPU CPU

mindspore.numpy.logical_and

Computes the truth value of x1 AND x2 element-wise.

Ascend GPU CPU

mindspore.numpy.logical_not

Computes the truth value of NOT a element-wise.

Ascend GPU CPU

mindspore.numpy.logical_or

Computes the truth value of x1 OR x2 element-wise.

Ascend GPU CPU

mindspore.numpy.logical_xor

Computes the truth value of x1 XOR x2, element-wise.

Ascend GPU CPU

mindspore.numpy.not_equal

Returns (x1 != x2) element-wise.

Ascend GPU CPU

mindspore.numpy.signbit

Returns element-wise True where signbit is set (less than zero).

Ascend GPU CPU

mindspore.numpy.sometrue

Tests whether any array element along a given axis evaluates to True.

Ascend GPU CPU

数学运算

数学运算类算子包括各类数学相关的运算:加减乘除乘方,以及指数、对数等常见函数等。

数学计算支持类似NumPy的广播特性。

  • 加法

    以下代码实现了 input_xinput_y 两数组相加的操作:

    input_x = np.full((3, 2), [1, 2])
    input_y = np.full((3, 2), [3, 4])
    output = np.add(input_x, input_y)
    print(output)
    

    运行结果如下:

    [[4 6]
     [4 6]
     [4 6]]
    
  • 矩阵乘法

    以下代码实现了 input_xinput_y 两矩阵相乘的操作:

    input_x = np.arange(2*3).reshape(2, 3).astype('float32')
    input_y = np.arange(3*4).reshape(3, 4).astype('float32')
    output = np.matmul(input_x, input_y)
    print(output)
    

    运行结果如下:

    [[20. 23. 26. 29.]
     [56. 68. 80. 92.]]
    
  • 求平均值

    以下代码实现了求 input_x 所有元素的平均值的操作:

    input_x = np.arange(6).astype('float32')
    output = np.mean(input_x)
    print(output)
    

    运行结果如下:

    2.5
    
  • 指数

    以下代码实现了自然常数 einput_x 次方的操作:

    input_x = np.arange(5).astype('float32')
    output = np.exp(input_x)
    print(output)
    

    运行结果如下:

    [ 1.         2.7182817  7.389056  20.085537  54.59815  ]
    

API Name

Description

Supported Platforms

mindspore.numpy.absolute

Calculates the absolute value element-wise.

Ascend GPU CPU

mindspore.numpy.add

Adds arguments element-wise.

Ascend GPU CPU

mindspore.numpy.amax

Returns the maximum of an array or maximum along an axis.

Ascend GPU CPU

mindspore.numpy.amin

Returns the minimum of an array or minimum along an axis.

Ascend GPU CPU

mindspore.numpy.arccos

Trigonometric inverse cosine, element-wise.

Ascend GPU CPU

mindspore.numpy.arccosh

Inverse hyperbolic cosine, element-wise.

Ascend GPU CPU

mindspore.numpy.arcsin

Inverse sine, element-wise.

Ascend GPU CPU

mindspore.numpy.arcsinh

Inverse hyperbolic sine element-wise.

Ascend GPU CPU

mindspore.numpy.arctan

Trigonometric inverse tangent, element-wise.

Ascend GPU CPU

mindspore.numpy.arctan2

Element-wise arc tangent of \(x1/x2\) choosing the quadrant correctly.

Ascend CPU GPU

mindspore.numpy.arctanh

Inverse hyperbolic tangent element-wise.

Ascend CPU

mindspore.numpy.argmax

Returns the indices of the maximum values along an axis.

Ascend GPU CPU

mindspore.numpy.argmin

Returns the indices of the minimum values along an axis.

Ascend GPU CPU

mindspore.numpy.around

Evenly round to the given number of decimals.

Ascend GPU CPU

mindspore.numpy.average

Computes the weighted average along the specified axis.

Ascend GPU CPU

mindspore.numpy.bincount

Count number of occurrences of each value in array of non-negative ints.

Ascend GPU CPU

mindspore.numpy.bitwise_and

Computes the bit-wise AND of two arrays element-wise.

Ascend CPU

mindspore.numpy.bitwise_or

Computes the bit-wise OR of two arrays element-wise.

Ascend CPU

mindspore.numpy.bitwise_xor

Computes the bit-wise XOR of two arrays element-wise.

Ascend CPU

mindspore.numpy.cbrt

Returns the cube-root of a tensor, element-wise.

Ascend GPU CPU

mindspore.numpy.ceil

Returns the ceiling of the input, element-wise.

Ascend GPU CPU

mindspore.numpy.clip

Clips (limits) the values in an array.

Ascend GPU CPU

mindspore.numpy.convolve

Returns the discrete, linear convolution of two one-dimensional sequences.

GPU CPU

mindspore.numpy.copysign

Changes the sign of x1 to that of x2, element-wise.

Ascend GPU CPU

mindspore.numpy.corrcoef

Returns Pearson product-moment correlation coefficients.

Ascend GPU CPU

mindspore.numpy.correlate

Cross-correlation of two 1-dimensional sequences.

GPU

mindspore.numpy.cos

Cosine element-wise.

Ascend GPU CPU

mindspore.numpy.cosh

Hyperbolic cosine, element-wise.

Ascend CPU

mindspore.numpy.count_nonzero

Counts the number of non-zero values in the tensor x.

Ascend GPU CPU

mindspore.numpy.cov

Estimates a covariance matrix, given data and weights.

Ascend GPU CPU

mindspore.numpy.cross

Returns the cross product of two (arrays of) vectors.

Ascend GPU CPU

mindspore.numpy.cumprod

Returns the cumulative product of elements along a given axis.

Ascend GPU

mindspore.numpy.cumsum

Returns the cumulative sum of the elements along a given axis.

Ascend GPU CPU

mindspore.numpy.deg2rad

Converts angles from degrees to radians.

Ascend GPU CPU

mindspore.numpy.diff

Calculates the n-th discrete difference along the given axis.

Ascend GPU CPU

mindspore.numpy.digitize

Returns the indices of the bins to which each value in input array belongs.

Ascend GPU CPU

mindspore.numpy.divide

Returns a true division of the inputs, element-wise.

Ascend GPU CPU

mindspore.numpy.divmod

Returns element-wise quotient and remainder simultaneously.

Ascend GPU CPU

mindspore.numpy.dot

Returns the dot product of two arrays.

Ascend GPU CPU

mindspore.numpy.ediff1d

The differences between consecutive elements of a tensor.

Ascend GPU CPU

mindspore.numpy.exp

Calculates the exponential of all elements in the input array.

Ascend GPU CPU

mindspore.numpy.exp2

Calculates 2**p for all p in the input array.

Ascend GPU CPU

mindspore.numpy.expm1

Calculates exp(x) - 1 for all elements in the array.

Ascend GPU CPU

mindspore.numpy.fix

Rounds to nearest integer towards zero.

Ascend GPU CPU

mindspore.numpy.float_power

First array elements raised to powers from second array, element-wise.

Ascend GPU CPU

mindspore.numpy.floor

Returns the floor of the input, element-wise.

Ascend GPU CPU

mindspore.numpy.floor_divide

Returns the largest integer smaller or equal to the division of the inputs.

Ascend GPU CPU

mindspore.numpy.fmod

Returns the element-wise remainder of division.

Ascend GPU CPU

mindspore.numpy.gcd

Returns the greatest common divisor of |x1| and |x2|.

Ascend GPU CPU

mindspore.numpy.gradient

Returns the gradient of a N-dimensional array.

Ascend GPU CPU

mindspore.numpy.heaviside

Computes the Heaviside step function.

Ascend GPU CPU

mindspore.numpy.histogram

Computes the histogram of a dataset.

Ascend GPU CPU

mindspore.numpy.histogram2d

Computes the multidimensional histogram of some data.

Ascend GPU CPU

mindspore.numpy.histogramdd

Computes the multidimensional histogram of some data.

Ascend GPU CPU

mindspore.numpy.hypot

Given the “legs” of a right triangle, returns its hypotenuse.

Ascend GPU CPU

mindspore.numpy.inner

Returns the inner product of two tensors.

Ascend GPU CPU

mindspore.numpy.interp

One-dimensional linear interpolation for monotonically increasing sample points.

Ascend GPU CPU

mindspore.numpy.invert

Computes bit-wise inversion, or bit-wise NOT, element-wise.

Ascend

mindspore.numpy.kron

Kronecker product of two arrays.

Ascend GPU CPU

mindspore.numpy.lcm

Returns the lowest common multiple of |x1| and |x2|.

Ascend GPU CPU

mindspore.numpy.log

Returns the natural logarithm, element-wise.

Ascend GPU CPU

mindspore.numpy.log10

Base-10 logarithm of x.

Ascend GPU CPU

mindspore.numpy.log1p

Returns the natural logarithm of one plus the input array, element-wise.

Ascend GPU CPU

mindspore.numpy.log2

Base-2 logarithm of x.

Ascend GPU CPU

mindspore.numpy.logaddexp

Logarithm of the sum of exponentiations of the inputs.

Ascend GPU CPU

mindspore.numpy.logaddexp2

Logarithm of the sum of exponentiations of the inputs in base of 2.

Ascend GPU CPU

mindspore.numpy.matmul

Returns the matrix product of two arrays.

Ascend GPU CPU

mindspore.numpy.matrix_power

Raises a square matrix to the (integer) power n.

Ascend GPU CPU

mindspore.numpy.maximum

Returns the element-wise maximum of array elements.

Ascend GPU CPU

mindspore.numpy.mean

Computes the arithmetic mean along the specified axis.

Ascend GPU CPU

mindspore.numpy.minimum

Element-wise minimum of tensor elements.

Ascend GPU CPU

mindspore.numpy.multi_dot

Computes the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order.

Ascend GPU CPU

mindspore.numpy.multiply

Multiplies arguments element-wise.

Ascend GPU CPU

mindspore.numpy.nancumsum

Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.

GPU CPU

mindspore.numpy.nanmax

Return the maximum of an array or maximum along an axis, ignoring any NaNs.

GPU CPU

mindspore.numpy.nanmean

Computes the arithmetic mean along the specified axis, ignoring NaNs.

GPU CPU

mindspore.numpy.nanmin

Returns the minimum of array elements over a given axis, ignoring any NaNs.

GPU CPU

mindspore.numpy.nanstd

Computes the standard deviation along the specified axis, while ignoring NaNs.

GPU CPU

mindspore.numpy.nansum

Returns the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.

GPU CPU

mindspore.numpy.nanvar

Computes the variance along the specified axis, while ignoring NaNs.

GPU CPU

mindspore.numpy.negative

Numerical negative, element-wise.

Ascend GPU CPU

mindspore.numpy.norm

Matrix or vector norm.

Ascend GPU CPU

mindspore.numpy.outer

Computes the outer product of two vectors.

Ascend GPU CPU

mindspore.numpy.polyadd

Finds the sum of two polynomials.

Ascend GPU CPU

mindspore.numpy.polyder

Returns the derivative of the specified order of a polynomial.

Ascend GPU CPU

mindspore.numpy.polyint

Returns an antiderivative (indefinite integral) of a polynomial.

Ascend GPU CPU

mindspore.numpy.polymul

Finds the product of two polynomials.

GPU

mindspore.numpy.polysub

Difference (subtraction) of two polynomials.

Ascend GPU CPU

mindspore.numpy.polyval

Evaluates a polynomial at specific values.

Ascend GPU CPU

mindspore.numpy.positive

Numerical positive, element-wise.

Ascend GPU CPU

mindspore.numpy.power

First array elements raised to powers from second array, element-wise.

Ascend GPU CPU

mindspore.numpy.promote_types

Returns the data type with the smallest size and smallest scalar kind.

Ascend GPU CPU

mindspore.numpy.ptp

Range of values (maximum - minimum) along an axis.

Ascend GPU CPU

mindspore.numpy.rad2deg

Converts angles from radians to degrees.

Ascend GPU CPU

mindspore.numpy.radians

Converts angles from degrees to radians.

Ascend GPU CPU

mindspore.numpy.ravel_multi_index

Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index.

GPU

mindspore.numpy.reciprocal

Returns the reciprocal of the argument, element-wise.

Ascend GPU CPU

mindspore.numpy.remainder

Returns element-wise remainder of division.

Ascend GPU CPU

mindspore.numpy.result_type

Returns the type that results from applying the type promotion rules to the arguments.

Ascend GPU CPU

mindspore.numpy.rint

Rounds elements of the array to the nearest integer.

Ascend GPU CPU

mindspore.numpy.searchsorted

Finds indices where elements should be inserted to maintain order.

Ascend GPU CPU

mindspore.numpy.sign

Returns an element-wise indication of the sign of a number.

Ascend GPU CPU

mindspore.numpy.sin

Trigonometric sine, element-wise.

Ascend GPU CPU

mindspore.numpy.sinh

Hyperbolic sine, element-wise.

Ascend CPU

mindspore.numpy.sqrt

Returns the non-negative square-root of an array, element-wise.

Ascend GPU CPU

mindspore.numpy.square

Returns the element-wise square of the input.

Ascend GPU CPU

mindspore.numpy.std

Computes the standard deviation along the specified axis.

Ascend GPU CPU

mindspore.numpy.subtract

Subtracts arguments, element-wise.

Ascend GPU CPU

mindspore.numpy.sum

Returns sum of array elements over a given axis.

Ascend GPU CPU

mindspore.numpy.tan

Computes tangent element-wise.

Ascend CPU

mindspore.numpy.tanh

Computes hyperbolic tangent element-wise.

Ascend GPU CPU

mindspore.numpy.tensordot

Computes tensor dot product along specified axes.

Ascend GPU CPU

mindspore.numpy.trapz

Integrates along the given axis using the composite trapezoidal rule.

Ascend GPU CPU

mindspore.numpy.true_divide

Returns a true division of the inputs, element-wise.

Ascend GPU CPU

mindspore.numpy.trunc

Returns the truncated value of the input, element-wise.

Ascend GPU CPU

mindspore.numpy.unwrap

Unwraps by changing deltas between values to 2*pi complement.

Ascend GPU CPU

mindspore.numpy.var

Computes the variance along the specified axis.

Ascend GPU CPU

MindSpore Numpy与MindSpore特性结合

mindspore.numpy能够充分利用MindSpore的强大功能,实现算子的自动微分,并使用图模式加速运算,帮助用户快速构建高效的模型。同时,MindSpore还支持多种后端设备,包括Ascend、GPU和CPU等,用户可以根据自己的需求灵活设置。以下提供了几种常用方法:

  • ms_function: 将代码包裹进图模式,用于提高代码运行效率。

  • GradOperation: 用于自动求导。

  • mindspore.context: 用于设置运行模式和后端设备等。

  • mindspore.nn.Cell: 用于建立深度学习模型。

使用示例如下:

  • ms_function使用示例

    首先,以神经网络里经常使用到的矩阵乘与矩阵加算子为例:

    import mindspore.numpy as np
    
    x = np.arange(8).reshape(2, 4).astype('float32')
    w1 = np.ones((4, 8))
    b1 = np.zeros((8,))
    w2 = np.ones((8, 16))
    b2 = np.zeros((16,))
    w3 = np.ones((16, 4))
    b3 = np.zeros((4,))
    
    def forward(x, w1, b1, w2, b2, w3, b3):
        x = np.dot(x, w1) + b1
        x = np.dot(x, w2) + b2
        x = np.dot(x, w3) + b3
        return x
    
    print(forward(x, w1, b1, w2, b2, w3, b3))
    

    运行结果如下:

    [[ 768.  768.  768.  768.]
     [2816. 2816. 2816. 2816.]]
    

    对上述示例,我们可以借助 ms_function 将所有算子编译到一张静态图里以加快运行效率,示例如下:

    from mindspore import ms_function
    
    forward_compiled = ms_function(forward)
    print(forward(x, w1, b1, w2, b2, w3, b3))
    

    运行结果如下:

    [[ 768.  768.  768.  768.]
     [2816. 2816. 2816. 2816.]]
    

    Note

    目前静态图不支持在Python交互式模式下运行,并且有部分语法限制。ms_function 的更多信息可参考 API ms_function

  • GradOperation使用示例

    GradOperation 可以实现自动求导。以下示例可以实现对上述没有用 ms_function 修饰的 forward 函数定义的计算求导。

    from mindspore import ops
    
    grad_all = ops.composite.GradOperation(get_all=True)
    print(grad_all(forward)(x, w1, b1, w2, b2, w3, b3))
    

    运行结果如下:

    (Tensor(shape=[2, 4], dtype=Float32, value=
     [[ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02,  5.12000000e+02],
      [ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02,  5.12000000e+02]]),
     Tensor(shape=[4, 8], dtype=Float32, value=
     [[ 2.56000000e+02,  2.56000000e+02,  2.56000000e+02 ...  2.56000000e+02,  2.56000000e+02,  2.56000000e+02],
      [ 3.84000000e+02,  3.84000000e+02,  3.84000000e+02 ...  3.84000000e+02,  3.84000000e+02,  3.84000000e+02],
      [ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02 ...  5.12000000e+02,  5.12000000e+02,  5.12000000e+02]
      [ 6.40000000e+02,  6.40000000e+02,  6.40000000e+02 ...  6.40000000e+02,  6.40000000e+02,  6.40000000e+02]]),
      ...
     Tensor(shape=[4], dtype=Float32, value= [ 2.00000000e+00,  2.00000000e+00,  2.00000000e+00,  2.00000000e+00]))
    

    如果要对 ms_function 修饰的 forward 计算求导,需要提前使用 context 设置运算模式为图模式,示例如下:

    from mindspore import ms_function, set_context, GRAPH_MODE
    
    set_context(mode=GRAPH_MODE)
    grad_all = ops.composite.GradOperation(get_all=True)
    print(grad_all(ms_function(forward))(x, w1, b1, w2, b2, w3, b3))
    

    运行结果如下:

    (Tensor(shape=[2, 4], dtype=Float32, value=
     [[ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02,  5.12000000e+02],
      [ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02,  5.12000000e+02]]),
     Tensor(shape=[4, 8], dtype=Float32, value=
     [[ 2.56000000e+02,  2.56000000e+02,  2.56000000e+02 ...  2.56000000e+02,  2.56000000e+02,  2.56000000e+02],
      [ 3.84000000e+02,  3.84000000e+02,  3.84000000e+02 ...  3.84000000e+02,  3.84000000e+02,  3.84000000e+02],
      [ 5.12000000e+02,  5.12000000e+02,  5.12000000e+02 ...  5.12000000e+02,  5.12000000e+02,  5.12000000e+02]
      [ 6.40000000e+02,  6.40000000e+02,  6.40000000e+02 ...  6.40000000e+02,  6.40000000e+02,  6.40000000e+02]]),
      ...
     Tensor(shape=[4], dtype=Float32, value= [ 2.00000000e+00,  2.00000000e+00,  2.00000000e+00,  2.00000000e+00]))
    

    更多细节可参考 API GradOperation

  • mindspore.context使用示例

    MindSpore支持多后端运算,可以通过 mindspore.context 进行设置。mindspore.numpy 的多数算子可以使用图模式或者PyNative模式运行,也可以运行在CPU,CPU或者Ascend等多种后端设备上。

    from mindspore import set_context, GRAPH_MODE, PYNATIVE_MODE
    
    # Execucation in static graph mode
    set_context(mode=GRAPH_MODE)
    
    # Execucation in PyNative mode
    set_context(mode=PYNATIVE_MODE)
    
    # Execucation on CPU backend
    set_context(device_target="CPU")
    
    # Execucation on GPU backend
    set_context(device_target="GPU")
    
    # Execucation on Ascend backend
    set_context(device_target="Ascend")
    ...
    

    更多细节可参考 API mindspore.set_context

  • mindspore.numpy使用示例

    这里提供一个使用 mindspore.numpy 构建网络模型的示例。

    mindspore.numpy 接口可以定义在 nn.Cell 代码块内进行网络的构建,示例如下:

    import mindspore.numpy as np
    from mindspore import set_context, GRAPH_MODE
    from mindspore.nn import Cell
    
    set_context(mode=GRAPH_MODE)
    
    x = np.arange(8).reshape(2, 4).astype('float32')
    w1 = np.ones((4, 8))
    b1 = np.zeros((8,))
    w2 = np.ones((8, 16))
    b2 = np.zeros((16,))
    w3 = np.ones((16, 4))
    b3 = np.zeros((4,))
    
    class NeuralNetwork(Cell):
        def construct(self, x, w1, b1, w2, b2, w3, b3):
            x = np.dot(x, w1) + b1
            x = np.dot(x, w2) + b2
            x = np.dot(x, w3) + b3
            return x
    
    net = NeuralNetwork()
    
    print(net(x, w1, b1, w2, b2, w3, b3))
    

    运行结果如下:

    [[ 768.  768.  768.  768.]
     [2816. 2816. 2816. 2816.]]