oneflow.nn =================================== Operators for neural networks ---------------------------------- .. currentmodule:: oneflow.nn .. automodule:: oneflow.nn :members: AdaptiveAvgPool1d, AdaptiveAvgPool2d, AdaptiveAvgPool3d, AvgPool1d, AvgPool2d, AvgPool3d, BCELoss, BCEWithLogitsLoss, BatchNorm1d, BatchNorm2d, BatchNorm3d, COCOReader, CTCLoss, CoinFlip, ConstantPad1d, ConstantPad2d, ConstantPad3d, Conv1d, Conv2d, Conv3d, ConvTranspose1d, ConvTranspose2d, ConvTranspose3d, CombinedMarginLoss, CropMirrorNormalize, CrossEntropyLoss, Dropout, ELU, CELU, Embedding, Flatten, GELU, GLU, GroupNorm, Hardsigmoid, Hardshrink, Hardswish, Hardtanh, Identity, InstanceNorm1d, InstanceNorm2d, InstanceNorm3d, KLDivLoss, L1Loss, LayerNorm, LeakyReLU, Linear, LogSigmoid, LogSoftmax, MSELoss, MarginRankingLoss, TripletMarginLoss, MaxPool1d, MaxPool2d, MaxPool3d, ModuleDict, ModuleList, Mish, NLLLoss, OFRecordImageDecoder, OFRecordImageDecoderRandomCrop, OFRecordRawDecoder, OFRecordReader, OFRecordBytesDecoder, PReLU, Parameter, ParameterDict, ParameterList, PixelShuffle, ReLU, ReLU6, ReflectionPad2d, ReplicationPad2d, Sequential, SELU, SiLU, Sigmoid, SmoothL1Loss, Softmax, Softplus, Softshrink, Softsign, Tanh, Threshold, Upsample, UpsamplingBilinear2d, UpsamplingNearest2d, ZeroPad2d, MinMaxObserver, MovingAverageMinMaxObserver, FakeQuantization, Quantization, FusedBatchNorm1d, FusedBatchNorm2d, FusedBatchNorm3d, FusedMLP, .. autofunction:: oneflow.nn.modules.pixelshuffle.PixelShufflev2 .. autofunction:: oneflow.nn.parallel.DistributedDataParallel .. currentmodule:: oneflow.nn.utils .. autofunction:: oneflow.nn.utils.clip_grad_norm_ .. autofunction:: oneflow.nn.utils.weight_norm .. autofunction:: oneflow.nn.utils.remove_weight_norm