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.

Sort and Search¶

`argmax`([data, axis, keepdims, out, name])	Returns indices of the maximum values along an axis.
`argmin`([data, axis, keepdims, out, name])	Returns indices of the minimum values along an axis.
`argsort`([data, axis, is_ascend, dtype, out, …])	Returns the indices that would sort an input array along the given axis.
`sort`([data, axis, is_ascend, out, name])	Returns a sorted copy of an input array along the given axis.
`topk`([data, axis, k, ret_typ, is_ascend, …])	Returns the top k elements in an input array along the given axis.

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`	Draw random samples from a discrete uniform distribution.
`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.

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, cudnn_off, …])	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`	Number of stored values for a sparse tensor, including explicit zeros.
`index_copy`	Copies the elements of a new_tensor into the old_tensor.

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.