mindspore.numpy

MindSpore Numpy package contains a set of Numpy-like interfaces, which allows developers to build models on MindSpore with similar syntax of Numpy.

MindSpore Numpy operators can be classified into four functional modules: array generation, array operation, logic operation and math operation.

Common imported modules in corresponding API examples are as follows:

import mindspore.numpy as np

Note

MindSpore numpy provides a consistent programming experience with native numpy by assembling the low-level operators. Compared with MindSpore’s function and ops interfaces, it is easier for user to understand and use. However, please notice that to be more compatible with native numpy, the performance of some MindSpore numpy interfaces may be weaker than the corresponding function/ops interfaces. Users can choose which to use as needed.

Array Generation

Array generation operators are used to generate tensors.

Here is an example to generate an array:

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))

The result is as follows:

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

Here we have more examples:

  • Generate a tensor filled with the same element

    np.full can be used to generate a tensor with user-specified values:

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

    The result is as follows:

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

    Here is another example to generate an array with the specified shape and filled with the value of 1:

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

    The result is as follows:

    [[1. 1. 1.]
     [1. 1. 1.]]
    
  • Generate tensors in a specified range

    Generate an arithmetic array within the specified range:

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

    The result is as follows:

    [0 1 2 3 4]
    
  • Generate tensors with specific requirement

    Generate a matrix where the lower elements are 1 and the upper elements are 0 on the given diagonal:

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

    The result is as follows:

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

    Another example, generate a 2-D matrix with a diagonal of 1 and other elements of 0:

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

    The result is as follows:

    [[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 Operation

Array operations focus on tensor manipulation.

  • Manipulate the shape of the tensor

    For example, transpose a matrix:

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

    The result is as follows:

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

    Another example, swap two axes:

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

    The result is as follows:

    (2, 1, 3)
    
  • Tensor splitting

    Divide the input tensor into multiple tensors equally, for example:

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

    The result is as follows:

    (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]))
    
  • Tensor combination

    Concatenate the two tensors according to the specified axis, for example:

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

    The result is as follows:

    [ 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.argwhere

Find the indices of Tensor elements that are non-zero, grouped by element.

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.intersect1d

Find the intersection of two Tensors.

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.setdiff1d

Find the set difference of two Tensors.

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

Logic

Logic operations define computations related with boolean types. Examples of equal and less operations are as follows:

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)

The result is as follows:

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

Math

Math operations include basic and advanced math operations on tensors, and they have full support on Numpy broadcasting rules. Here are some examples:

  • Sum two tensors

    The following code implements the operation of adding two tensors of input_x and input_y:

    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)
    

    The result is as follows:

    [[4 6]
     [4 6]
     [4 6]]
    
  • Matrics multiplication

    The following code implements the operation of multiplying two matrices input_x and input_y:

    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)
    

    The result is as follows:

    [[20. 23. 26. 29.]
     [56. 68. 80. 92.]]
    
  • Take the average along a given axis

    The following code implements the operation of averaging all the elements of input_x:

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

    The result is as follows:

    2.5
    
  • Exponential arithmetic

    The following code implements the operation of the natural constant e to the power of input_x:

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

    The result is as follows:

    [ 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 GPU CPU

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

Interact With MindSpore Functions

Since mindspore.numpy directly wraps MindSpore tensors and operators, it has all the advantages and properties of MindSpore. In this section, we will briefly introduce how to employ MindSpore execution management and automatic differentiation in mindspore.numpy coding scenarios. These include:

  • jit decorator: for running codes in static graph mode for better efficiency.

  • GradOperation: for automatic gradient computation.

  • mindspore.set_context: for mindspore.numpy execution management.

  • mindspore.nn.Cell: for using mindspore.numpy interfaces in MindSpore Deep Learning Models.

The following are examples:

  • Use jit decorator to run code in static graph mode

    Let’s first see an example consisted of matrix multiplication and bias add, which is a typical process in Neural Networks:

    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))
    

    The result is as follows:

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

    In this function, MindSpore dispatches each computing kernel to device separately. However, with the help of jit decorator, we can compile all operations into a single static computing graph.

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

    The result is as follows:

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

    Note

    Currently, static graph cannot run in Python interactive mode and not all python types can be passed into functions decorated with jit.

  • Use GradOperation to compute deratives

    GradOperation can be used to take deratives from normal functions and functions decorated with jit. Take the previous example:

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

    The result is as follows:

    (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]))
    

    To take the gradient of jit compiled functions, first we need to set the execution mode to static graph mode.

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

    The result is as follows:

    (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]))
    

    For more details, see API GradOperation .

  • Use mindspore.set_context to control execution mode

    Most functions in mindspore.numpy can run in Graph Mode and PyNative Mode, and can run on CPU, GPU and Ascend. Like MindSpore, users can manage the execution mode using mindspore.set_context

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

    For more details, see API mindspore.set_context .

  • Use mindspore.numpy in MindSpore Deep Learning Models

    mindspore.numpy interfaces can be used inside nn.cell blocks as well. For example, the above code can be modified to:

    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))
    

    The result is as follows:

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