Neural Network Cell
For building predefined building blocks or computational units in neural networks.
Compared with the previous version, the added, deleted and supported platforms change information of mindspore.nn operators in MindSpore, please refer to the link https://gitee.com/mindspore/docs/blob/r1.8/resource/api_updates/nn_api_updates.md.
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.Cell mindspore.nn.GraphCell mindspore.nn.LossBase mindspore.nn.Optimizer
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.CellList mindspore.nn.SequentialCell
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.DistributedGradReducer mindspore.nn.DynamicLossScaleUpdateCell mindspore.nn.FixedLossScaleUpdateCell mindspore.nn.ForwardValueAndGrad mindspore.nn.GetNextSingleOp mindspore.nn.MicroBatchInterleaved mindspore.nn.ParameterUpdate mindspore.nn.PipelineCell mindspore.nn.TimeDistributed mindspore.nn.TrainOneStepCell mindspore.nn.TrainOneStepWithLossScaleCell mindspore.nn.WithEvalCell mindspore.nn.WithGradCell mindspore.nn.WithLossCell
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.Conv1d mindspore.nn.Conv1dTranspose mindspore.nn.Conv2d mindspore.nn.Conv2dTranspose mindspore.nn.Conv3d mindspore.nn.Conv3dTranspose mindspore.nn.Unfold
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.RNN mindspore.nn.RNNCell mindspore.nn.GRU mindspore.nn.GRUCell mindspore.nn.LSTM mindspore.nn.LSTMCell
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.Embedding mindspore.nn.EmbeddingLookup mindspore.nn.MultiFieldEmbeddingLookup
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.CELU mindspore.nn.ELU mindspore.nn.FastGelu mindspore.nn.GELU mindspore.nn.Hardtanh mindspore.nn.HShrink mindspore.nn.HSigmoid mindspore.nn.HSwish mindspore.nn.LeakyReLU mindspore.nn.LogSigmoid mindspore.nn.LogSoftmax mindspore.nn.LRN mindspore.nn.Mish mindspore.nn.Softsign mindspore.nn.PReLU mindspore.nn.ReLU mindspore.nn.ReLU6 mindspore.nn.RReLU mindspore.nn.SeLU mindspore.nn.SiLU mindspore.nn.Sigmoid mindspore.nn.Softmax mindspore.nn.SoftShrink mindspore.nn.Tanh mindspore.nn.Tanhshrink mindspore.nn.Threshold
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.Dense mindspore.nn.BiDense
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.Dropout mindspore.nn.Dropout2d mindspore.nn.Dropout3d
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.BatchNorm1d mindspore.nn.BatchNorm2d mindspore.nn.BatchNorm3d mindspore.nn.GlobalBatchNorm mindspore.nn.GroupNorm mindspore.nn.InstanceNorm1d mindspore.nn.InstanceNorm2d mindspore.nn.InstanceNorm3d mindspore.nn.LayerNorm mindspore.nn.SyncBatchNorm
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.AdaptiveAvgPool1d mindspore.nn.AdaptiveAvgPool2d mindspore.nn.AdaptiveMaxPool1d mindspore.nn.AdaptiveMaxPool2d mindspore.nn.AvgPool1d mindspore.nn.AvgPool2d mindspore.nn.MaxPool1d mindspore.nn.MaxPool2d
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.Pad mindspore.nn.ConstantPad1d mindspore.nn.ConstantPad2d mindspore.nn.ConstantPad3d mindspore.nn.ReflectionPad1d mindspore.nn.ReflectionPad2d mindspore.nn.ZeroPad2d
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.BCELoss mindspore.nn.BCEWithLogitsLoss mindspore.nn.CosineEmbeddingLoss mindspore.nn.CrossEntropyLoss mindspore.nn.DiceLoss mindspore.nn.FocalLoss mindspore.nn.HuberLoss mindspore.nn.L1Loss mindspore.nn.MSELoss mindspore.nn.MultiClassDiceLoss mindspore.nn.NLLLoss mindspore.nn.RMSELoss mindspore.nn.SampledSoftmaxLoss mindspore.nn.SmoothL1Loss mindspore.nn.SoftMarginLoss mindspore.nn.SoftmaxCrossEntropyWithLogits
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.Adadelta mindspore.nn.Adagrad mindspore.nn.Adam mindspore.nn.AdaMax mindspore.nn.AdamOffload mindspore.nn.AdamWeightDecay mindspore.nn.AdaSumByDeltaWeightWrapCell mindspore.nn.AdaSumByGradWrapCell mindspore.nn.ASGD mindspore.nn.FTRL mindspore.nn.Lamb mindspore.nn.LARS mindspore.nn.LazyAdam mindspore.nn.Momentum mindspore.nn.ProximalAdagrad mindspore.nn.RMSProp mindspore.nn.Rprop mindspore.nn.SGD mindspore.nn.thor
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.Accuracy mindspore.nn.auc mindspore.nn.BleuScore mindspore.nn.ConfusionMatrix mindspore.nn.ConfusionMatrixMetric mindspore.nn.CosineSimilarity mindspore.nn.Dice mindspore.nn.F1 mindspore.nn.Fbeta mindspore.nn.HausdorffDistance mindspore.nn.get_metric_fn mindspore.nn.Loss mindspore.nn.MAE mindspore.nn.MeanSurfaceDistance mindspore.nn.Metric mindspore.nn.MSE mindspore.nn.names mindspore.nn.OcclusionSensitivity mindspore.nn.Perplexity mindspore.nn.Precision mindspore.nn.Recall mindspore.nn.ROC mindspore.nn.RootMeanSquareDistance mindspore.nn.rearrange_inputs mindspore.nn.Top1CategoricalAccuracy mindspore.nn.Top5CategoricalAccuracy mindspore.nn.TopKCategoricalAccuracy
The dynamic learning rates in this module are all subclasses of LearningRateSchedule. Pass the instance of LearningRateSchedule to an optimizer. During the training process, the optimizer calls the instance taking current step as input to get the current learning rate.
import mindspore.nn as nn min_lr = 0.01 max_lr = 0.1 decay_steps = 4 cosine_decay_lr = nn.CosineDecayLR(min_lr, max_lr, decay_steps) net = Net() optim = nn.Momentum(net.trainable_params(), learning_rate=cosine_decay_lr, momentum=0.9)
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.CosineDecayLR mindspore.nn.ExponentialDecayLR mindspore.nn.InverseDecayLR mindspore.nn.NaturalExpDecayLR mindspore.nn.PolynomialDecayLR mindspore.nn.WarmUpLR
The dynamic learning rates in this module are all functions. Call the function and pass the result to an optimizer. During the training process, the optimizer takes result[current step] as current learning rate.
import mindspore.nn as nn min_lr = 0.01 max_lr = 0.1 total_step = 6 step_per_epoch = 1 decay_epoch = 4 lr= nn.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch) net = Net() optim = nn.Momentum(net.trainable_params(), learning_rate=lr, momentum=0.9)
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.cosine_decay_lr mindspore.nn.exponential_decay_lr mindspore.nn.inverse_decay_lr mindspore.nn.natural_exp_decay_lr mindspore.nn.piecewise_constant_lr mindspore.nn.polynomial_decay_lr mindspore.nn.warmup_lr
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.SparseTensorDenseMatmul mindspore.nn.SparseToDense
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.CentralCrop mindspore.nn.ImageGradients mindspore.nn.MSSSIM mindspore.nn.PSNR mindspore.nn.ResizeBilinear mindspore.nn.SSIM
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.MatrixDiag mindspore.nn.MatrixDiagPart mindspore.nn.MatrixSetDiag
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.ClipByNorm mindspore.nn.Flatten mindspore.nn.get_activation mindspore.nn.L1Regularizer mindspore.nn.Norm mindspore.nn.OneHot mindspore.nn.Range mindspore.nn.Roll mindspore.nn.Tril mindspore.nn.Triu
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.MatMul mindspore.nn.Moments mindspore.nn.ReduceLogSumExp
.. msplatformautosummary:: :toctree: nn :nosignatures: :template: classtemplate.rst mindspore.nn.Jvp mindspore.nn.Vjp
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