Table Of Contents
Table Of Contents

Routines

Create Arrays

arange Returns evenly spaced values within a given interval.
array Creates an array from any object exposing the array interface.
diag Extracts a diagonal or constructs a diagonal array.
empty Returns a new array of given shape and type, without initializing entries.
full Returns a new array of given shape and type, filled with the given value val.
load Loads an array from file.
ones Returns a new array filled with all ones, with the given shape and type.
ones_like Return an array of ones with the same shape and type as the input array.
save Saves a list of arrays or a dict of str->array to file.
zeros Return a new array of given shape and type, filled with zeros.
zeros_like Return an array of zeros with the same shape, type and storage type as the input array.

Manipulate

Change shape and type

cast Casts all elements of the input to a new type.
flatten Flattens the input array into a 2-D array by collapsing the higher dimensions.
expand_dims Inserts a new axis of size 1 into the array shape
reshape Reshapes the input array.
reshape_like Reshape some or all dimensions of lhs to have the same shape as some or all dimensions of rhs.
shape_array Returns a 1D int64 array containing the shape of data.
size_array Returns a 1D int64 array containing the size of data.

Expand elements

broadcast_axes Broadcasts the input array over particular axes.
broadcast_like Broadcasts lhs to have the same shape as rhs.
broadcast_to Broadcasts the input array to a new shape.
pad Pads an input array with a constant or edge values of the array.
repeat Repeats elements of an array.
tile Repeats the whole array multiple times.

Rearrange elements

depth_to_space Rearranges(permutes) data from depth into blocks of spatial data.
flip Reverses the order of elements along given axis while preserving array shape.
space_to_depth Rearranges(permutes) blocks of spatial data into depth.
swapaxes Interchanges two axes of an array.
transpose Permutes the dimensions of an array.

Join and split

concat Joins input arrays along a given axis.
split Splits an array along a particular axis into multiple sub-arrays.
stack Join a sequence of arrays along a new axis.

Index

batch_take Takes elements from a data batch.
one_hot Returns a one-hot array.
pick Picks elements from an input array according to the input indices along the given axis.
ravel_multi_index Converts a batch of index arrays into an array of flat indices.
slice Slices a region of the array.
slice_axis Slices along a given axis.
slice_like Slices a region of the array like the shape of another array.
take Takes elements from an input array along the given axis.
unravel_index Converts an array of flat indices into a batch of index arrays.
where Return the elements, either from x or y, depending on the condition.

Sequence

SequenceLast([data, sequence_length, …]) Takes the last element of a sequence.
SequenceMask([data, sequence_length, …]) Sets all elements outside the sequence to a constant value.
SequenceReverse([data, sequence_length, …]) Reverses the elements of each sequence.

Math

Arithmetic

add Returns element-wise sum of the input arrays with broadcasting.
add_n Adds all input arguments element-wise.
batch_dot Batchwise dot product.
divide Returns element-wise division of the input arrays with broadcasting.
dot Dot product of two arrays.
modulo Returns element-wise modulo of the input arrays with broadcasting.
multiply Returns element-wise product of the input arrays with broadcasting.
negative Numerical negative of the argument, element-wise.
subtract Returns element-wise difference of the input arrays with broadcasting.

Trigonometric

arccos Returns element-wise inverse cosine of the input array.
arcsin Returns element-wise inverse sine of the input array.
arctan Returns element-wise inverse tangent of the input array.
broadcast_hypot Returns the hypotenuse of a right angled triangle, given its “legs” with broadcasting.
degrees Converts each element of the input array from radians to degrees.
cos Computes the element-wise cosine of the input array.
radians Converts each element of the input array from degrees to radians.
sin Computes the element-wise sine of the input array.
tan Computes the element-wise tangent of the input array.

Hyperbolic

arcsinh Returns the element-wise inverse hyperbolic sine of the input array, computed element-wise.
arccosh Returns the element-wise inverse hyperbolic cosine of the input array, computed element-wise.
arctanh Returns the element-wise inverse hyperbolic tangent of the input array, computed element-wise.
cosh Returns the hyperbolic cosine of the input array, computed element-wise.
sinh Returns the hyperbolic sine of the input array, computed element-wise.
tanh Returns the hyperbolic tangent of the input array, computed element-wise.

Reduce

max Computes the max of array elements over given axes.
min Computes the min of array elements over given axes.
mean Computes the mean of array elements over given axes.
nanprod Computes the product of array elements over given axes treating Not a Numbers (NaN) as one.
nansum Computes the sum of array elements over given axes treating Not a Numbers (NaN) as zero.
norm Computes the norm on an NDArray.
prod Computes the product of array elements over given axes.
sum Computes the sum of array elements over given axes.

Round

ceil Returns element-wise ceiling of the input.
fix Returns element-wise rounded value to the nearest integer towards zero of the input.
floor Returns element-wise floor of the input.
round Returns element-wise rounded value to the nearest integer of the input.
rint Returns element-wise rounded value to the nearest integer of the input.
trunc Return the element-wise truncated value of the input.

Exponents and logarithms

exp Returns element-wise exponential value of the input.
expm1 Returns exp(x) - 1 computed element-wise on the input.
log Returns element-wise Natural logarithmic value of the input.
log1p Returns element-wise log(1 + x) value of the input.
log10 Returns element-wise Base-10 logarithmic value of the input.
log2 Returns element-wise Base-2 logarithmic value of the input.

Powers

cbrt Returns element-wise cube-root value of the input.
power Returns result of first array elements raised to powers from second array, element-wise with broadcasting.
rcbrt Returns element-wise inverse cube-root value of the input.
reciprocal Returns the reciprocal of the argument, element-wise.
rsqrt Returns element-wise inverse square-root value of the input.
square Returns element-wise squared value of the input.
sqrt Returns element-wise square-root value of the input.

Compare

equal Returns the result of element-wise equal to (==) comparison operation with broadcasting.
greater Returns the result of element-wise greater than (>) comparison operation with broadcasting.
greater_equal Returns the result of element-wise greater than or equal to (>=) comparison operation with broadcasting.
lesser Returns the result of element-wise lesser than (<) comparison operation with broadcasting.
lesser_equal Returns the result of element-wise lesser than or equal to (<=) comparison operation with broadcasting.
not_equal Returns the result of element-wise not equal to (!=) comparison operation with broadcasting.

Logical

logical_and Returns the result of element-wise logical and comparison operation with broadcasting.
logical_not Returns the result of logical NOT (!) function
logical_or Returns the result of element-wise logical or comparison operation with broadcasting.
logical_xor Returns the result of element-wise logical xor comparison operation with broadcasting.

Random Distribution

random.exponential Draw samples from an exponential distribution.
random.gamma Draw random samples from a gamma distribution.
random.generalized_negative_binomial Draw random samples from a generalized negative binomial distribution.
random.multinomial Concurrent sampling from multiple multinomial distributions.
random.negative_binomial Draw random samples from a negative binomial distribution.
random.normal Draw random samples from a normal (Gaussian) distribution.
random.poisson Draw random samples from a Poisson distribution.
random.randint Return random integer in range [a, b], including both end points.
random.randn Draw random samples from a normal (Gaussian) distribution.
random.shuffle Shuffle the elements randomly.
random.uniform Draw random samples from a uniform distribution.
mxnet.random.seed Seeds the random number generators in MXNet.

Linear Algebra

linalg.gelqf LQ factorization for general matrix.
linalg.gemm Performs general matrix multiplication and accumulation.
linalg.gemm2 Performs general matrix multiplication.
linalg.potrf Performs Cholesky factorization of a symmetric positive-definite matrix.
linalg.potri Performs matrix inversion from a Cholesky factorization.
linalg.sumlogdiag Computes the sum of the logarithms of the diagonal elements of a square matrix.
linalg.syevd Eigendecomposition for symmetric matrix.
linalg.syrk Multiplication of matrix with its transpose.
linalg.trmm Performs multiplication with a lower triangular matrix.
linalg.trsm Solves matrix equation involving a lower triangular matrix.

Miscellaneous

abs([data, out, name]) Returns element-wise absolute value of the input.
clip([data, a_min, a_max, out, name]) Clips (limits) the values in an array.
gamma([data, out, name]) Returns the gamma function (extension of the factorial function to the reals), computed element-wise on the input array.
gammaln([data, out, name]) Returns element-wise log of the absolute value of the gamma function of the input.
maximum(lhs, rhs) Returns element-wise maximum of the input arrays with broadcasting.
minimum(lhs, rhs) Returns element-wise minimum of the input arrays with broadcasting.
sign([data, out, name]) Returns element-wise sign of the input.

Neural Network

Activation([data, act_type, out, name]) Applies an activation function element-wise to the input.
BatchNorm([data, gamma, beta, moving_mean, …]) Batch normalization.
BilinearSampler([data, grid, out, name]) Applies bilinear sampling to input feature map.
BlockGrad([data, out, name]) Stops gradient computation.
Convolution([data, weight, bias, kernel, …]) Compute N-D convolution on (N+2)-D input.
Correlation([data1, data2, kernel_size, …]) Applies correlation to inputs.
Custom(*data, **kwargs) Apply a custom operator implemented in a frontend language (like Python).
Deconvolution([data, weight, bias, kernel, …]) Computes 1D or 2D transposed convolution (aka fractionally strided convolution) of the input tensor.
Dropout([data, p, mode, axes, out, name]) Applies dropout operation to input array.
Embedding([data, weight, input_dim, …]) Maps integer indices to vector representations (embeddings).
FullyConnected([data, weight, bias, …]) Applies a linear transformation: \(Y = XW^T + b\).
GridGenerator([data, transform_type, …]) Generates 2D sampling grid for bilinear sampling.
IdentityAttachKLSparseReg([data, …]) Apply a sparse regularization to the output a sigmoid activation function.
InstanceNorm([data, gamma, beta, eps, out, name]) Applies instance normalization to the n-dimensional input array.
L2Normalization([data, eps, mode, out, name]) Normalize the input array using the L2 norm.
LayerNorm([data, gamma, beta, axis, eps, …]) Layer normalization.
LeakyReLU([data, gamma, act_type, slope, …]) Applies Leaky rectified linear unit activation element-wise to the input.
LinearRegressionOutput([data, label, …]) Computes and optimizes for squared loss during backward propagation.
log_softmax([data, axis, temperature, out, name]) Computes the log softmax of the input.
LogisticRegressionOutput([data, label, …]) Applies a logistic function to the input.
LRN([data, alpha, beta, knorm, nsize, out, name]) Applies local response normalization to the input.
MAERegressionOutput([data, label, …]) Computes mean absolute error of the input.
MakeLoss([data, grad_scale, valid_thresh, …]) Make your own loss function in network construction.
Pooling([data, kernel, pool_type, …]) Performs pooling on the input.
relu([data, out, name]) Computes rectified linear.
ROIPooling([data, rois, pooled_size, …]) Performs region of interest(ROI) pooling on the input array.
RNN([data, parameters, state, state_cell, …]) Applies recurrent layers to input data.
sigmoid([data, out, name]) Computes sigmoid of x element-wise.
smooth_l1([data, scalar, out, name]) Calculate Smooth L1 Loss(lhs, scalar) by summing
softmax([data, axis, temperature, out, name]) Applies the softmax function.
softmax_cross_entropy([data, label, out, name]) Calculate cross entropy of softmax output and one-hot label.
SoftmaxOutput([data, label, grad_scale, …]) Computes the gradient of cross entropy loss with respect to softmax output.
SoftmaxActivation([data, mode, out, name]) Applies softmax activation to input.
SpatialTransformer([data, loc, …]) Applies a spatial transformer to input feature map.
SVMOutput([data, label, margin, …]) Computes support vector machine based transformation of the input.
UpSampling(*data, **kwargs) Performs nearest neighbor/bilinear up sampling to inputs.

Contributed routines

Contrib NDArray API of MXNet.

Note

This package contains experimental APIs and may change in the near future.

Manipulate

count_sketch Apply CountSketch to input: map a d-dimension data to k-dimension data”
getnnz
index_copy

FFT

fft Apply 1D FFT to input”
ifft Apply 1D ifft to input”

Quantization

dequantize Dequantize the input tensor into a float tensor.
quantize Quantize a input tensor from float to out_type, with user-specified min_range and max_range.

Neural network

AdaptiveAvgPooling2D Applies a 2D adaptive average pooling over a 4D input with the shape of (NCHW).
BilinearResize2D Perform 2D resizing (upsampling or downsampling) for 4D input using bilinear interpolation.
ctc_loss Connectionist Temporal Classification Loss.
DeformableConvolution Compute 2-D deformable convolution on 4-D input.
DeformablePSROIPooling Performs deformable position-sensitive region-of-interest pooling on inputs.
MultiBoxDetection Convert multibox detection predictions.
MultiBoxPrior Generate prior(anchor) boxes from data, sizes and ratios.
MultiBoxTarget Compute Multibox training targets
MultiProposal Generate region proposals via RPN
Proposal Generate region proposals via RPN
PSROIPooling Performs region-of-interest pooling on inputs.
ROIAlign This operator takes a 4D feature map as an input array and region proposals as rois, then align the feature map over sub-regions of input and produces a fixed-sized output array.

Control flow

cond Run an if-then-else using user-defined condition and computation
foreach Run a for loop with user-defined computation over NDArrays on dimension 0.
while_loop Run a while loop with user-defined computation and loop condition.