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Table Of Contents
  • Python Tutorials
    • Getting Started
      • Crash Course
        • Introduction
        • Step 1: Manipulate data with NP on MXNet
        • Step 2: Create a neural network
        • Step 3: Automatic differentiation with autograd
        • Step 4: Necessary components that are not in the network
        • Step 5: Datasets and DataLoader
        • Using own data with included Datasets
        • Using your own data with custom Datasets
        • New in MXNet 2.0: faster C++ backend dataloaders
        • Step 6: Train a Neural Network
        • Step 7: Load and Run a NN using GPU
      • Moving to MXNet from Other Frameworks
        • PyTorch vs Apache MXNet
      • Gluon: from experiment to deployment
      • Gluon2.0: Migration Guide
      • Logistic regression explained
      • MNIST
    • Packages
      • Automatic Differentiation
      • Gluon
        • Blocks
          • Custom Layers
          • Hybridize
          • Initialization
          • Parameter and Block Naming
          • Layers and Blocks
          • Parameter Management
          • Saving and Loading Gluon Models
          • Activation Blocks
        • Data Tutorials
          • Image Augmentation
          • Gluon Datasets and DataLoader
          • Using own data with included Datasets
          • Using own data with custom Datasets
          • Appendix: Upgrading from Module DataIter to Gluon DataLoader
        • Image Tutorials
          • Image similarity search with InfoGAN
          • Handwritten Digit Recognition
        • Losses
          • Custom Loss Blocks
          • Kullback-Leibler (KL) Divergence
          • Loss functions
        • Text Tutorials
          • Google Neural Machine Translation
          • Machine Translation with Transformer
        • Training
          • MXNet Gluon Fit API
          • Trainer
          • Learning Rates
            • Learning Rate Finder
            • Learning Rate Schedules
            • Advanced Learning Rate Schedules
          • Normalization Blocks
      • KVStore
        • Distributed Key-Value Store
      • Legacy
        • NDArray
          • An Intro: Manipulate Data the MXNet Way with NDArray
          • NDArray Operations
          • NDArray Contexts
          • Gotchas using NumPy in Apache MXNet
          • Tutorials
            • CSRNDArray - NDArray in Compressed Sparse Row Storage Format
            • RowSparseNDArray - NDArray for Sparse Gradient Updates
      • What is NP on MXNet
        • The NP on MXNet cheat sheet
        • Differences between NP on MXNet and NumPy
      • ONNX
        • Fine-tuning an ONNX model
        • Running inference on MXNet/Gluon from an ONNX model
        • Export ONNX Models
      • Optimizers
      • Visualization
        • Visualize networks
    • Performance
      • Compression
        • Deploy with int-8
        • Float16
        • Gradient Compression
        • GluonCV with Quantized Models
      • Accelerated Backend Tools
        • oneDNN
          • Install MXNet with oneDNN
          • oneDNN Quantization
          • Improving accuracy with Intel® Neural Compressor
        • Use TVM
        • Profiling MXNet Models
        • Using AMP: Automatic Mixed Precision
    • Deployment
      • Export
        • Exporting to ONNX format
        • Export Gluon CV Models
        • Save / Load Parameters
      • Inference
        • Deploy into C++
        • Image Classication using pretrained ResNet-50 model on Jetson module
      • Run on AWS
        • Run on an EC2 Instance
        • Run on Amazon SageMaker
        • MXNet on the Cloud
    • Extend
      • Custom Numpy Operators
      • New Operator Creation
      • New Operator in MXNet Backend
      • Using RTC for CUDA kernels
  • Python API
    • mxnet.np
      • Array objects
        • The N-dimensional array (ndarray)
        • Indexing
      • Routines
        • Array creation routines
          • mxnet.np.eye
          • mxnet.np.empty
          • mxnet.np.full
          • mxnet.np.identity
          • mxnet.np.ones
          • mxnet.np.ones_like
          • mxnet.np.zeros
          • mxnet.np.zeros_like
          • mxnet.np.array
          • mxnet.np.copy
          • mxnet.np.arange
          • mxnet.np.linspace
          • mxnet.np.logspace
          • mxnet.np.meshgrid
          • mxnet.np.tril
        • Array manipulation routines
          • mxnet.np.reshape
          • mxnet.np.ravel
          • mxnet.np.ndarray.flatten
          • mxnet.np.swapaxes
          • mxnet.np.ndarray.T
          • mxnet.np.transpose
          • mxnet.np.moveaxis
          • mxnet.np.rollaxis
          • mxnet.np.expand_dims
          • mxnet.np.squeeze
          • mxnet.np.broadcast_to
          • mxnet.np.broadcast_arrays
          • mxnet.np.atleast_1d
          • mxnet.np.atleast_2d
          • mxnet.np.atleast_3d
          • mxnet.np.concatenate
          • mxnet.np.stack
          • mxnet.np.dstack
          • mxnet.np.vstack
          • mxnet.np.column_stack
          • mxnet.np.hstack
          • mxnet.np.split
          • mxnet.np.hsplit
          • mxnet.np.vsplit
          • mxnet.np.array_split
          • mxnet.np.dsplit
          • mxnet.np.tile
          • mxnet.np.repeat
          • mxnet.np.unique
          • mxnet.np.delete
          • mxnet.np.insert
          • mxnet.np.append
          • mxnet.np.resize
          • mxnet.np.trim_zeros
          • mxnet.np.reshape
          • mxnet.np.flip
          • mxnet.np.roll
          • mxnet.np.rot90
          • mxnet.np.fliplr
          • mxnet.np.flipud
        • Input and output
          • mxnet.np.genfromtxt
          • mxnet.np.ndarray.tolist
          • mxnet.np.set_printoptions
        • Linear algebra (numpy.linalg)
          • mxnet.np.dot
          • mxnet.np.vdot
          • mxnet.np.inner
          • mxnet.np.outer
          • mxnet.np.tensordot
          • mxnet.np.einsum
          • mxnet.np.linalg.multi_dot
          • mxnet.np.matmul
          • mxnet.np.linalg.matrix_power
          • mxnet.np.kron
          • mxnet.np.linalg.svd
          • mxnet.np.linalg.cholesky
          • mxnet.np.linalg.qr
          • mxnet.np.linalg.eig
          • mxnet.np.linalg.eigh
          • mxnet.np.linalg.eigvals
          • mxnet.np.linalg.eigvalsh
          • mxnet.np.linalg.norm
          • mxnet.np.trace
          • mxnet.np.linalg.cond
          • mxnet.np.linalg.det
          • mxnet.np.linalg.matrix_rank
          • mxnet.np.linalg.slogdet
          • mxnet.np.linalg.solve
          • mxnet.np.linalg.tensorsolve
          • mxnet.np.linalg.lstsq
          • mxnet.np.linalg.inv
          • mxnet.np.linalg.pinv
          • mxnet.np.linalg.tensorinv
        • Mathematical functions
          • mxnet.np.sin
          • mxnet.np.cos
          • mxnet.np.tan
          • mxnet.np.arcsin
          • mxnet.np.arccos
          • mxnet.np.arctan
          • mxnet.np.degrees
          • mxnet.np.radians
          • mxnet.np.hypot
          • mxnet.np.arctan2
          • mxnet.np.deg2rad
          • mxnet.np.rad2deg
          • mxnet.np.unwrap
          • mxnet.np.sinh
          • mxnet.np.cosh
          • mxnet.np.tanh
          • mxnet.np.arcsinh
          • mxnet.np.arccosh
          • mxnet.np.arctanh
          • mxnet.np.rint
          • mxnet.np.fix
          • mxnet.np.floor
          • mxnet.np.ceil
          • mxnet.np.trunc
          • mxnet.np.around
          • mxnet.np.round_
          • mxnet.np.sum
          • mxnet.np.prod
          • mxnet.np.cumsum
          • mxnet.np.nanprod
          • mxnet.np.nansum
          • mxnet.np.cumprod
          • mxnet.np.nancumprod
          • mxnet.np.nancumsum
          • mxnet.np.diff
          • mxnet.np.ediff1d
          • mxnet.np.cross
          • mxnet.np.trapz
          • mxnet.np.exp
          • mxnet.np.expm1
          • mxnet.np.log
          • mxnet.np.log10
          • mxnet.np.log2
          • mxnet.np.log1p
          • mxnet.np.logaddexp
          • mxnet.np.i0
          • mxnet.np.ldexp
          • mxnet.np.signbit
          • mxnet.np.copysign
          • mxnet.np.frexp
          • mxnet.np.spacing
          • mxnet.np.lcm
          • mxnet.np.gcd
          • mxnet.np.add
          • mxnet.np.reciprocal
          • mxnet.np.negative
          • mxnet.np.divide
          • mxnet.np.power
          • mxnet.np.subtract
          • mxnet.np.mod
          • mxnet.np.multiply
          • mxnet.np.true_divide
          • mxnet.np.remainder
          • mxnet.np.positive
          • mxnet.np.float_power
          • mxnet.np.fmod
          • mxnet.np.modf
          • mxnet.np.divmod
          • mxnet.np.floor_divide
          • mxnet.np.clip
          • mxnet.np.sqrt
          • mxnet.np.cbrt
          • mxnet.np.square
          • mxnet.np.absolute
          • mxnet.np.sign
          • mxnet.np.maximum
          • mxnet.np.minimum
          • mxnet.np.fabs
          • mxnet.np.heaviside
          • mxnet.np.fmax
          • mxnet.np.fmin
          • mxnet.np.nan_to_num
          • mxnet.np.interp
        • np.random
          • mxnet.np.random.choice
          • mxnet.np.random.shuffle
          • mxnet.np.random.normal
          • mxnet.np.random.uniform
          • mxnet.np.random.rand
          • mxnet.np.random.randint
          • mxnet.np.random.beta
          • mxnet.np.random.chisquare
          • mxnet.np.random.exponential
          • mxnet.np.random.f
          • mxnet.np.random.gamma
          • mxnet.np.random.gumbel
          • mxnet.np.random.laplace
          • mxnet.np.random.logistic
          • mxnet.np.random.lognormal
          • mxnet.np.random.multinomial
          • mxnet.np.random.multivariate_normal
          • mxnet.np.random.pareto
          • mxnet.np.random.power
          • mxnet.np.random.rayleigh
          • mxnet.np.random.weibull
        • Sorting, searching, and counting
          • mxnet.np.ndarray.sort
          • mxnet.np.sort
          • mxnet.np.lexsort
          • mxnet.np.argsort
          • mxnet.np.msort
          • mxnet.np.partition
          • mxnet.np.argpartition
          • mxnet.np.argmax
          • mxnet.np.argmin
          • mxnet.np.nanargmax
          • mxnet.np.nanargmin
          • mxnet.np.argwhere
          • mxnet.np.nonzero
          • mxnet.np.flatnonzero
          • mxnet.np.where
          • mxnet.np.searchsorted
          • mxnet.np.extract
          • mxnet.np.count_nonzero
        • Statistics
          • mxnet.np.min
          • mxnet.np.max
          • mxnet.np.amin
          • mxnet.np.amax
          • mxnet.np.nanmin
          • mxnet.np.nanmax
          • mxnet.np.ptp
          • mxnet.np.percentile
          • mxnet.np.nanpercentile
          • mxnet.np.quantile
          • mxnet.np.nanquantile
          • mxnet.np.mean
          • mxnet.np.std
          • mxnet.np.var
          • mxnet.np.median
          • mxnet.np.average
          • mxnet.np.nanmedian
          • mxnet.np.nanstd
          • mxnet.np.nanvar
          • mxnet.np.corrcoef
          • mxnet.np.correlate
          • mxnet.np.cov
          • mxnet.np.histogram
          • mxnet.np.histogram2d
          • mxnet.np.histogramdd
          • mxnet.np.bincount
          • mxnet.np.histogram_bin_edges
          • mxnet.np.digitize
    • NPX: NumPy Neural Network Extension
      • mxnet.npx.set_np
      • mxnet.npx.reset_np
      • mxnet.npx.cpu
      • mxnet.npx.cpu_pinned
      • mxnet.npx.gpu
      • mxnet.npx.gpu_memory_info
      • mxnet.npx.current_device
      • mxnet.npx.num_gpus
      • mxnet.npx.activation
      • mxnet.npx.batch_norm
      • mxnet.npx.convolution
      • mxnet.npx.dropout
      • mxnet.npx.embedding
      • mxnet.npx.fully_connected
      • mxnet.npx.layer_norm
      • mxnet.npx.pooling
      • mxnet.npx.rnn
      • mxnet.npx.leaky_relu
      • mxnet.npx.multibox_detection
      • mxnet.npx.multibox_prior
      • mxnet.npx.multibox_target
      • mxnet.npx.roi_pooling
      • mxnet.npx.sigmoid
      • mxnet.npx.relu
      • mxnet.npx.smooth_l1
      • mxnet.npx.softmax
      • mxnet.npx.log_softmax
      • mxnet.npx.topk
      • mxnet.npx.waitall
      • mxnet.npx.load
      • mxnet.npx.save
      • mxnet.npx.one_hot
      • mxnet.npx.pick
      • mxnet.npx.reshape_like
      • mxnet.npx.batch_flatten
      • mxnet.npx.batch_dot
      • mxnet.npx.gamma
      • mxnet.npx.sequence_mask
    • mxnet.gluon
      • gluon.Block
      • gluon.HybridBlock
      • gluon.SymbolBlock
      • gluon.Constant
      • gluon.Parameter
      • gluon.Trainer
      • gluon.contrib
      • gluon.data
        • data.vision
          • vision.datasets
          • vision.transforms
      • gluon.loss
      • gluon.metric
      • gluon.model_zoo.vision
      • gluon.nn
      • gluon.rnn
      • gluon.utils
    • mxnet.autograd
    • mxnet.initializer
    • mxnet.optimizer
    • mxnet.lr_scheduler
    • KVStore: Communication for Distributed Training
    • Horovod
      • mxnet.kvstore.Horovod
    • BytePS
      • mxnet.kvstore.BytePS
    • KVStore Interface
      • mxnet.kvstore.KVStore
      • mxnet.kvstore.KVStoreBase
      • mxnet.kvstore.KVStoreServer
    • mxnet.contrib
      • contrib.io
      • contrib.ndarray
      • contrib.onnx
      • contrib.quantization
      • contrib.symbol
      • contrib.tensorboard
      • contrib.tensorrt
      • contrib.text
    • Legacy
      • mxnet.callback
      • mxnet.image
      • mxnet.io
      • mxnet.ndarray
        • ndarray
        • ndarray.contrib
        • ndarray.image
        • ndarray.linalg
        • ndarray.op
        • ndarray.random
        • ndarray.register
        • ndarray.sparse
        • ndarray.utils
      • mxnet.recordio
      • mxnet.symbol
        • symbol
        • symbol.contrib
        • symbol.image
        • symbol.linalg
        • symbol.op
        • symbol.random
        • symbol.register
        • symbol.sparse
      • mxnet.visualization
    • mxnet.device
    • mxnet.engine
    • mxnet.executor
    • mxnet.kvstore_server
    • mxnet.profiler
    • mxnet.rtc
    • mxnet.runtime
      • mxnet.runtime.Feature
      • mxnet.runtime.Features
      • mxnet.runtime.feature_list
    • mxnet.test_utils
    • mxnet.util
Table Of Contents
  • Python Tutorials
    • Getting Started
      • Crash Course
        • Introduction
        • Step 1: Manipulate data with NP on MXNet
        • Step 2: Create a neural network
        • Step 3: Automatic differentiation with autograd
        • Step 4: Necessary components that are not in the network
        • Step 5: Datasets and DataLoader
        • Using own data with included Datasets
        • Using your own data with custom Datasets
        • New in MXNet 2.0: faster C++ backend dataloaders
        • Step 6: Train a Neural Network
        • Step 7: Load and Run a NN using GPU
      • Moving to MXNet from Other Frameworks
        • PyTorch vs Apache MXNet
      • Gluon: from experiment to deployment
      • Gluon2.0: Migration Guide
      • Logistic regression explained
      • MNIST
    • Packages
      • Automatic Differentiation
      • Gluon
        • Blocks
          • Custom Layers
          • Hybridize
          • Initialization
          • Parameter and Block Naming
          • Layers and Blocks
          • Parameter Management
          • Saving and Loading Gluon Models
          • Activation Blocks
        • Data Tutorials
          • Image Augmentation
          • Gluon Datasets and DataLoader
          • Using own data with included Datasets
          • Using own data with custom Datasets
          • Appendix: Upgrading from Module DataIter to Gluon DataLoader
        • Image Tutorials
          • Image similarity search with InfoGAN
          • Handwritten Digit Recognition
        • Losses
          • Custom Loss Blocks
          • Kullback-Leibler (KL) Divergence
          • Loss functions
        • Text Tutorials
          • Google Neural Machine Translation
          • Machine Translation with Transformer
        • Training
          • MXNet Gluon Fit API
          • Trainer
          • Learning Rates
            • Learning Rate Finder
            • Learning Rate Schedules
            • Advanced Learning Rate Schedules
          • Normalization Blocks
      • KVStore
        • Distributed Key-Value Store
      • Legacy
        • NDArray
          • An Intro: Manipulate Data the MXNet Way with NDArray
          • NDArray Operations
          • NDArray Contexts
          • Gotchas using NumPy in Apache MXNet
          • Tutorials
            • CSRNDArray - NDArray in Compressed Sparse Row Storage Format
            • RowSparseNDArray - NDArray for Sparse Gradient Updates
      • What is NP on MXNet
        • The NP on MXNet cheat sheet
        • Differences between NP on MXNet and NumPy
      • ONNX
        • Fine-tuning an ONNX model
        • Running inference on MXNet/Gluon from an ONNX model
        • Export ONNX Models
      • Optimizers
      • Visualization
        • Visualize networks
    • Performance
      • Compression
        • Deploy with int-8
        • Float16
        • Gradient Compression
        • GluonCV with Quantized Models
      • Accelerated Backend Tools
        • oneDNN
          • Install MXNet with oneDNN
          • oneDNN Quantization
          • Improving accuracy with Intel® Neural Compressor
        • Use TVM
        • Profiling MXNet Models
        • Using AMP: Automatic Mixed Precision
    • Deployment
      • Export
        • Exporting to ONNX format
        • Export Gluon CV Models
        • Save / Load Parameters
      • Inference
        • Deploy into C++
        • Image Classication using pretrained ResNet-50 model on Jetson module
      • Run on AWS
        • Run on an EC2 Instance
        • Run on Amazon SageMaker
        • MXNet on the Cloud
    • Extend
      • Custom Numpy Operators
      • New Operator Creation
      • New Operator in MXNet Backend
      • Using RTC for CUDA kernels
  • Python API
    • mxnet.np
      • Array objects
        • The N-dimensional array (ndarray)
        • Indexing
      • Routines
        • Array creation routines
          • mxnet.np.eye
          • mxnet.np.empty
          • mxnet.np.full
          • mxnet.np.identity
          • mxnet.np.ones
          • mxnet.np.ones_like
          • mxnet.np.zeros
          • mxnet.np.zeros_like
          • mxnet.np.array
          • mxnet.np.copy
          • mxnet.np.arange
          • mxnet.np.linspace
          • mxnet.np.logspace
          • mxnet.np.meshgrid
          • mxnet.np.tril
        • Array manipulation routines
          • mxnet.np.reshape
          • mxnet.np.ravel
          • mxnet.np.ndarray.flatten
          • mxnet.np.swapaxes
          • mxnet.np.ndarray.T
          • mxnet.np.transpose
          • mxnet.np.moveaxis
          • mxnet.np.rollaxis
          • mxnet.np.expand_dims
          • mxnet.np.squeeze
          • mxnet.np.broadcast_to
          • mxnet.np.broadcast_arrays
          • mxnet.np.atleast_1d
          • mxnet.np.atleast_2d
          • mxnet.np.atleast_3d
          • mxnet.np.concatenate
          • mxnet.np.stack
          • mxnet.np.dstack
          • mxnet.np.vstack
          • mxnet.np.column_stack
          • mxnet.np.hstack
          • mxnet.np.split
          • mxnet.np.hsplit
          • mxnet.np.vsplit
          • mxnet.np.array_split
          • mxnet.np.dsplit
          • mxnet.np.tile
          • mxnet.np.repeat
          • mxnet.np.unique
          • mxnet.np.delete
          • mxnet.np.insert
          • mxnet.np.append
          • mxnet.np.resize
          • mxnet.np.trim_zeros
          • mxnet.np.reshape
          • mxnet.np.flip
          • mxnet.np.roll
          • mxnet.np.rot90
          • mxnet.np.fliplr
          • mxnet.np.flipud
        • Input and output
          • mxnet.np.genfromtxt
          • mxnet.np.ndarray.tolist
          • mxnet.np.set_printoptions
        • Linear algebra (numpy.linalg)
          • mxnet.np.dot
          • mxnet.np.vdot
          • mxnet.np.inner
          • mxnet.np.outer
          • mxnet.np.tensordot
          • mxnet.np.einsum
          • mxnet.np.linalg.multi_dot
          • mxnet.np.matmul
          • mxnet.np.linalg.matrix_power
          • mxnet.np.kron
          • mxnet.np.linalg.svd
          • mxnet.np.linalg.cholesky
          • mxnet.np.linalg.qr
          • mxnet.np.linalg.eig
          • mxnet.np.linalg.eigh
          • mxnet.np.linalg.eigvals
          • mxnet.np.linalg.eigvalsh
          • mxnet.np.linalg.norm
          • mxnet.np.trace
          • mxnet.np.linalg.cond
          • mxnet.np.linalg.det
          • mxnet.np.linalg.matrix_rank
          • mxnet.np.linalg.slogdet
          • mxnet.np.linalg.solve
          • mxnet.np.linalg.tensorsolve
          • mxnet.np.linalg.lstsq
          • mxnet.np.linalg.inv
          • mxnet.np.linalg.pinv
          • mxnet.np.linalg.tensorinv
        • Mathematical functions
          • mxnet.np.sin
          • mxnet.np.cos
          • mxnet.np.tan
          • mxnet.np.arcsin
          • mxnet.np.arccos
          • mxnet.np.arctan
          • mxnet.np.degrees
          • mxnet.np.radians
          • mxnet.np.hypot
          • mxnet.np.arctan2
          • mxnet.np.deg2rad
          • mxnet.np.rad2deg
          • mxnet.np.unwrap
          • mxnet.np.sinh
          • mxnet.np.cosh
          • mxnet.np.tanh
          • mxnet.np.arcsinh
          • mxnet.np.arccosh
          • mxnet.np.arctanh
          • mxnet.np.rint
          • mxnet.np.fix
          • mxnet.np.floor
          • mxnet.np.ceil
          • mxnet.np.trunc
          • mxnet.np.around
          • mxnet.np.round_
          • mxnet.np.sum
          • mxnet.np.prod
          • mxnet.np.cumsum
          • mxnet.np.nanprod
          • mxnet.np.nansum
          • mxnet.np.cumprod
          • mxnet.np.nancumprod
          • mxnet.np.nancumsum
          • mxnet.np.diff
          • mxnet.np.ediff1d
          • mxnet.np.cross
          • mxnet.np.trapz
          • mxnet.np.exp
          • mxnet.np.expm1
          • mxnet.np.log
          • mxnet.np.log10
          • mxnet.np.log2
          • mxnet.np.log1p
          • mxnet.np.logaddexp
          • mxnet.np.i0
          • mxnet.np.ldexp
          • mxnet.np.signbit
          • mxnet.np.copysign
          • mxnet.np.frexp
          • mxnet.np.spacing
          • mxnet.np.lcm
          • mxnet.np.gcd
          • mxnet.np.add
          • mxnet.np.reciprocal
          • mxnet.np.negative
          • mxnet.np.divide
          • mxnet.np.power
          • mxnet.np.subtract
          • mxnet.np.mod
          • mxnet.np.multiply
          • mxnet.np.true_divide
          • mxnet.np.remainder
          • mxnet.np.positive
          • mxnet.np.float_power
          • mxnet.np.fmod
          • mxnet.np.modf
          • mxnet.np.divmod
          • mxnet.np.floor_divide
          • mxnet.np.clip
          • mxnet.np.sqrt
          • mxnet.np.cbrt
          • mxnet.np.square
          • mxnet.np.absolute
          • mxnet.np.sign
          • mxnet.np.maximum
          • mxnet.np.minimum
          • mxnet.np.fabs
          • mxnet.np.heaviside
          • mxnet.np.fmax
          • mxnet.np.fmin
          • mxnet.np.nan_to_num
          • mxnet.np.interp
        • np.random
          • mxnet.np.random.choice
          • mxnet.np.random.shuffle
          • mxnet.np.random.normal
          • mxnet.np.random.uniform
          • mxnet.np.random.rand
          • mxnet.np.random.randint
          • mxnet.np.random.beta
          • mxnet.np.random.chisquare
          • mxnet.np.random.exponential
          • mxnet.np.random.f
          • mxnet.np.random.gamma
          • mxnet.np.random.gumbel
          • mxnet.np.random.laplace
          • mxnet.np.random.logistic
          • mxnet.np.random.lognormal
          • mxnet.np.random.multinomial
          • mxnet.np.random.multivariate_normal
          • mxnet.np.random.pareto
          • mxnet.np.random.power
          • mxnet.np.random.rayleigh
          • mxnet.np.random.weibull
        • Sorting, searching, and counting
          • mxnet.np.ndarray.sort
          • mxnet.np.sort
          • mxnet.np.lexsort
          • mxnet.np.argsort
          • mxnet.np.msort
          • mxnet.np.partition
          • mxnet.np.argpartition
          • mxnet.np.argmax
          • mxnet.np.argmin
          • mxnet.np.nanargmax
          • mxnet.np.nanargmin
          • mxnet.np.argwhere
          • mxnet.np.nonzero
          • mxnet.np.flatnonzero
          • mxnet.np.where
          • mxnet.np.searchsorted
          • mxnet.np.extract
          • mxnet.np.count_nonzero
        • Statistics
          • mxnet.np.min
          • mxnet.np.max
          • mxnet.np.amin
          • mxnet.np.amax
          • mxnet.np.nanmin
          • mxnet.np.nanmax
          • mxnet.np.ptp
          • mxnet.np.percentile
          • mxnet.np.nanpercentile
          • mxnet.np.quantile
          • mxnet.np.nanquantile
          • mxnet.np.mean
          • mxnet.np.std
          • mxnet.np.var
          • mxnet.np.median
          • mxnet.np.average
          • mxnet.np.nanmedian
          • mxnet.np.nanstd
          • mxnet.np.nanvar
          • mxnet.np.corrcoef
          • mxnet.np.correlate
          • mxnet.np.cov
          • mxnet.np.histogram
          • mxnet.np.histogram2d
          • mxnet.np.histogramdd
          • mxnet.np.bincount
          • mxnet.np.histogram_bin_edges
          • mxnet.np.digitize
    • NPX: NumPy Neural Network Extension
      • mxnet.npx.set_np
      • mxnet.npx.reset_np
      • mxnet.npx.cpu
      • mxnet.npx.cpu_pinned
      • mxnet.npx.gpu
      • mxnet.npx.gpu_memory_info
      • mxnet.npx.current_device
      • mxnet.npx.num_gpus
      • mxnet.npx.activation
      • mxnet.npx.batch_norm
      • mxnet.npx.convolution
      • mxnet.npx.dropout
      • mxnet.npx.embedding
      • mxnet.npx.fully_connected
      • mxnet.npx.layer_norm
      • mxnet.npx.pooling
      • mxnet.npx.rnn
      • mxnet.npx.leaky_relu
      • mxnet.npx.multibox_detection
      • mxnet.npx.multibox_prior
      • mxnet.npx.multibox_target
      • mxnet.npx.roi_pooling
      • mxnet.npx.sigmoid
      • mxnet.npx.relu
      • mxnet.npx.smooth_l1
      • mxnet.npx.softmax
      • mxnet.npx.log_softmax
      • mxnet.npx.topk
      • mxnet.npx.waitall
      • mxnet.npx.load
      • mxnet.npx.save
      • mxnet.npx.one_hot
      • mxnet.npx.pick
      • mxnet.npx.reshape_like
      • mxnet.npx.batch_flatten
      • mxnet.npx.batch_dot
      • mxnet.npx.gamma
      • mxnet.npx.sequence_mask
    • mxnet.gluon
      • gluon.Block
      • gluon.HybridBlock
      • gluon.SymbolBlock
      • gluon.Constant
      • gluon.Parameter
      • gluon.Trainer
      • gluon.contrib
      • gluon.data
        • data.vision
          • vision.datasets
          • vision.transforms
      • gluon.loss
      • gluon.metric
      • gluon.model_zoo.vision
      • gluon.nn
      • gluon.rnn
      • gluon.utils
    • mxnet.autograd
    • mxnet.initializer
    • mxnet.optimizer
    • mxnet.lr_scheduler
    • KVStore: Communication for Distributed Training
    • Horovod
      • mxnet.kvstore.Horovod
    • BytePS
      • mxnet.kvstore.BytePS
    • KVStore Interface
      • mxnet.kvstore.KVStore
      • mxnet.kvstore.KVStoreBase
      • mxnet.kvstore.KVStoreServer
    • mxnet.contrib
      • contrib.io
      • contrib.ndarray
      • contrib.onnx
      • contrib.quantization
      • contrib.symbol
      • contrib.tensorboard
      • contrib.tensorrt
      • contrib.text
    • Legacy
      • mxnet.callback
      • mxnet.image
      • mxnet.io
      • mxnet.ndarray
        • ndarray
        • ndarray.contrib
        • ndarray.image
        • ndarray.linalg
        • ndarray.op
        • ndarray.random
        • ndarray.register
        • ndarray.sparse
        • ndarray.utils
      • mxnet.recordio
      • mxnet.symbol
        • symbol
        • symbol.contrib
        • symbol.image
        • symbol.linalg
        • symbol.op
        • symbol.random
        • symbol.register
        • symbol.sparse
      • mxnet.visualization
    • mxnet.device
    • mxnet.engine
    • mxnet.executor
    • mxnet.kvstore_server
    • mxnet.profiler
    • mxnet.rtc
    • mxnet.runtime
      • mxnet.runtime.Feature
      • mxnet.runtime.Features
      • mxnet.runtime.feature_list
    • mxnet.test_utils
    • mxnet.util

Losses¶

  • Custom Loss Blocks
  • Kullback-Leibler (KL) Divergence
  • Loss functions

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A flexible and efficient library for deep learning.

Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.

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