Object

org.platanios.tensorflow.api.tf

learn

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object learn extends API

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Type Members

  1. type Activation = learn.layers.Activation

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  2. type AddBias = learn.layers.AddBias

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  3. type AudioSummary = learn.layers.AudioSummary

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  4. type BasicLSTMCell = learn.layers.rnn.cell.BasicLSTMCell

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  5. type BasicRNNCell = learn.layers.rnn.cell.BasicRNNCell

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  6. type BasicTuple = Tuple[ops.Output, ops.Output]

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  7. type BidirectionalRNN[O, OS, S, SS] = learn.layers.rnn.BidirectionalRNN[O, OS, S, SS]

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  8. type Cast = learn.layers.Cast

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  9. type CheckpointSaver = learn.hooks.CheckpointSaver

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  10. type ClipGradients = learn.ClipGradients

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  11. type ClipGradientsByAverageNorm = learn.ClipGradientsByAverageNorm

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  12. type ClipGradientsByGlobalNorm = learn.ClipGradientsByGlobalNorm

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  13. type ClipGradientsByNorm = learn.ClipGradientsByNorm

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  14. type ClipGradientsByValue = learn.ClipGradientsByValue

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  15. type Compose[T, R, S] = learn.layers.Compose[T, R, S]

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  16. type Concatenate[T, R] = learn.layers.Concatenate[T, R]

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  17. type Configuration = learn.Configuration

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  18. type Conv2D = learn.layers.Conv2D

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  19. type DeviceWrapper[O, OS, S, SS] = learn.layers.rnn.cell.DeviceWrapper[O, OS, S, SS]

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  20. type Dropout = learn.layers.Dropout

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  21. type DropoutWrapper[O, OS, S, SS] = learn.layers.rnn.cell.DropoutWrapper[O, OS, S, SS]

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  22. type Embedding = learn.layers.Embedding

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  23. type Estimator[IT, IO, ID, IS, I, TT, TO, TD, TS, EI] = learn.estimators.Estimator[IT, IO, ID, IS, I, TT, TO, TD, TS, EI]

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  24. type Evaluator[IT, IO, ID, IS, I, TT, TO, TD, TS, EI] = learn.hooks.Evaluator[IT, IO, ID, IS, I, TT, TO, TD, TS, EI]

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  25. type FileBasedEstimator[IT, IO, ID, IS, I, TT, TO, TD, TS, EI] = learn.estimators.FileBasedEstimator[IT, IO, ID, IS, I, TT, TO, TD, TS, EI]

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  26. type Flatten = learn.layers.Flatten

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  27. type GRUCell = learn.layers.rnn.cell.GRUCell

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  28. type HistogramSummary = learn.layers.HistogramSummary

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  29. type Hook = learn.hooks.Hook

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  30. type Identity[T] = learn.layers.Identity[T]

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  31. type ImageSummary = learn.layers.ImageSummary

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  32. type InMemoryEstimator[IT, IO, ID, IS, I, TT, TO, TD, TS, EI] = learn.estimators.InMemoryEstimator[IT, IO, ID, IS, I, TT, TO, TD, TS, EI]

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  33. type InferenceModel[IT, IO, ID, IS, I] = learn.InferenceModel[IT, IO, ID, IS, I]

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  34. type Input[T, O, DA, D, S] = learn.layers.Input[T, O, DA, D, S]

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  35. type L2Loss = learn.layers.L2Loss

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  36. type LSTMCell = learn.layers.rnn.cell.LSTMCell

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  37. type LSTMState = ops.rnn.cell.LSTMState

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  38. type LSTMTuple = Tuple[ops.Output, LSTMState]

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  39. type Layer[T, R] = learn.layers.Layer[T, R]

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  40. type Linear = learn.layers.Linear

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  41. type LogPoissonLoss = learn.layers.LogPoissonLoss

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  42. type LogSoftmax = learn.layers.LogSoftmax

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  43. type Loss[T] = learn.layers.Loss[T]

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  44. type LossLogger = learn.hooks.LossLogger

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  45. type Map[T, R, S, CC[A] <: TraversableLike[A, CC[A]]] = learn.layers.Map[T, R, S, CC]

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  46. type MaxPool = learn.layers.MaxPool

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  47. type Mean = learn.layers.Mean

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  48. type Mode = learn.Mode

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  49. type Model = learn.Model

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  50. type ModelDependentHook[IT, IO, ID, IS, I, TT, TO, TD, TS, EI] = learn.hooks.ModelDependentHook[IT, IO, ID, IS, I, TT, TO, TD, TS, EI]

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  51. type MultiCell[O, OS, S, SS] = learn.layers.rnn.cell.MultiCell[O, OS, S, SS]

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  52. type NaNChecker = learn.hooks.NaNChecker

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  53. type OneHot = learn.layers.OneHot

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  54. type RNN[O, OS, S, SS] = learn.layers.rnn.RNN[O, OS, S, SS]

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  55. type RNNCell[O, OS, S, SS] = learn.layers.rnn.cell.RNNCell[O, OS, S, SS]

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  56. type RNNTuple[O, S] = Tuple[O, S]

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  57. type ResidualWrapper[O, OS, S, SS] = learn.layers.rnn.cell.ResidualWrapper[O, OS, S, SS]

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  58. type ScalarSummary = learn.layers.ScalarSummary

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  59. type SequenceLoss = learn.layers.SequenceLoss

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  60. type SigmoidCrossEntropy = learn.layers.SigmoidCrossEntropy

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  61. type Softmax = learn.layers.Softmax

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  62. type SoftmaxCrossEntropy = learn.layers.SoftmaxCrossEntropy

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  63. type SparseSoftmaxCrossEntropy = learn.layers.SparseSoftmaxCrossEntropy

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  64. type Squeeze = learn.layers.Squeeze

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  65. type StepRateLogger = learn.hooks.StepRateLogger

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  66. type StopCriteria = learn.StopCriteria

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  67. type Stopper = learn.hooks.Stopper

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  68. type Sum = learn.layers.Sum

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  69. type SummarySaver = learn.hooks.SummarySaver

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  70. type SummaryWriterHookAddOn = learn.hooks.SummaryWriterHookAddOn

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  71. type SupervisedTrainableModel[IT, IO, ID, IS, I, TT, TO, TD, TS, T] = learn.SupervisedTrainableModel[IT, IO, ID, IS, I, TT, TO, TD, TS, T]

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  72. type TensorBoardHook = learn.hooks.TensorBoardHook

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  73. type TensorLogger = learn.hooks.TensorLogger

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  74. type TrainableModel[IT, IO, ID, IS, I, TT, TO, TD, TS, EI] = learn.TrainableModel[IT, IO, ID, IS, I, TT, TO, TD, TS, EI]

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  75. type Transpose = learn.layers.Transpose

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  76. type TriggeredHook = learn.hooks.TriggeredHook

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  77. type UnsupervisedTrainableModel[IT, IO, ID, IS, I] = learn.UnsupervisedTrainableModel[IT, IO, ID, IS, I]

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Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. val AddBias: learn.layers.AddBias.type

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  5. val AudioSummary: learn.layers.AudioSummary.type

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  6. val BasicLSTMCell: learn.layers.rnn.cell.BasicLSTMCell.type

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  7. val BasicRNNCell: learn.layers.rnn.cell.BasicRNNCell.type

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  8. val BidirectionalRNN: learn.layers.rnn.BidirectionalRNN.type

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  9. val CReLU: learn.layers.CReLU.type

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  10. val Cast: learn.layers.Cast.type

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  11. val CheckpointSaver: learn.hooks.CheckpointSaver.type

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  12. val ClipGradientsByAverageNorm: learn.ClipGradientsByAverageNorm.type

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  13. val ClipGradientsByGlobalNorm: learn.ClipGradientsByGlobalNorm.type

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  14. val ClipGradientsByNorm: learn.ClipGradientsByNorm.type

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  15. val ClipGradientsByValue: learn.ClipGradientsByValue.type

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  16. val Configuration: learn.Configuration.type

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  17. val Conv2D: learn.layers.Conv2D.type

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  18. val DeviceWrapper: learn.layers.rnn.cell.DeviceWrapper.type

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  19. val Dropout: learn.layers.Dropout.type

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  20. val DropoutWrapper: learn.layers.rnn.cell.DropoutWrapper.type

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  21. val ELU: learn.layers.ELU.type

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  22. val EVALUATION: learn.EVALUATION.type

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  23. val Embedding: learn.layers.Embedding.type

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  24. val Estimator: learn.estimators.Estimator.type

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  25. val Evaluator: learn.hooks.Evaluator.type

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  26. val FileBasedEstimator: learn.estimators.FileBasedEstimator.type

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  27. val Flatten: learn.layers.Flatten.type

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  28. val GRUCell: learn.layers.rnn.cell.GRUCell.type

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  29. val HistogramSummary: learn.layers.HistogramSummary.type

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  30. val INFERENCE: learn.INFERENCE.type

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  31. val Identity: learn.layers.Identity.type

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  32. val ImageSummary: learn.layers.ImageSummary.type

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  33. val InMemoryEstimator: learn.estimators.InMemoryEstimator.type

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  34. val Input: learn.layers.Input.type

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  35. val L2Loss: learn.layers.L2Loss.type

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  36. val LSTMCell: learn.layers.rnn.cell.LSTMCell.type

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  37. val LSTMState: learn.layers.rnn.cell.LSTMState.type

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  38. def LSTMTuple(output: ops.Output, state: LSTMState): LSTMTuple

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  39. val Linear: learn.layers.Linear.type

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  40. val LogPoissonLoss: learn.layers.LogPoissonLoss.type

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  41. val LogSoftmax: learn.layers.LogSoftmax.type

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  42. val LossLogger: learn.hooks.LossLogger.type

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  43. val MaxPool: learn.layers.MaxPool.type

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  44. val Mean: learn.layers.Mean.type

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  45. val Model: learn.Model.type

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  46. val MultiCell: learn.layers.rnn.cell.MultiCell.type

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  47. val NaNChecker: learn.hooks.NaNChecker.type

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  48. val NoClipGradients: learn.NoClipGradients.type

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  49. val NoHookTrigger: learn.hooks.NoHookTrigger.type

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  50. val OneHot: learn.layers.OneHot.type

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  51. val RNN: learn.layers.rnn.RNN.type

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  52. val RNNTuple: Tuple.type

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  53. val ReLU: learn.layers.ReLU.type

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  54. val ReLU6: learn.layers.ReLU6.type

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  55. val ResidualWrapper: learn.layers.rnn.cell.ResidualWrapper.type

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  56. val SELU: learn.layers.SELU.type

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  57. val ScalarSummary: learn.layers.ScalarSummary.type

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  58. val SequenceLoss: learn.layers.SequenceLoss.type

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  59. val Sigmoid: learn.layers.Sigmoid.type

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  60. val SigmoidCrossEntropy: learn.layers.SigmoidCrossEntropy.type

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  61. val Softmax: learn.layers.Softmax.type

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  62. val SoftmaxCrossEntropy: learn.layers.SoftmaxCrossEntropy.type

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  63. val Softplus: learn.layers.Softplus.type

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  64. val Softsign: learn.layers.Softsign.type

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  65. val SparseSoftmaxCrossEntropy: learn.layers.SparseSoftmaxCrossEntropy.type

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  66. val Squeeze: learn.layers.Squeeze.type

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  67. val StepHookTrigger: learn.hooks.StepHookTrigger.type

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  68. val StepRateLogger: learn.hooks.StepRateLogger.type

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  69. val StopCriteria: learn.StopCriteria.type

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  70. val Stopper: learn.hooks.Stopper.type

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  71. val Sum: learn.layers.Sum.type

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  72. val SummarySaver: learn.hooks.SummarySaver.type

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  73. val TRAINING: learn.TRAINING.type

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  74. val TensorBoardConfig: config.TensorBoardConfig.type

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  75. val TensorBoardHook: learn.hooks.TensorBoardHook.type

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  76. val TensorLogger: learn.hooks.TensorLogger.type

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  77. val TimeHookTrigger: learn.hooks.TimeHookTrigger.type

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  78. val Transpose: learn.layers.Transpose.type

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  79. final def asInstanceOf[T0]: T0

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  80. def clone(): AnyRef

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  81. def createWith[R](nameScope: String = null, device: String = "", deviceFunction: (ops.OpSpecification) ⇒ String = op => op.device)(block: ⇒ R): R

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  82. def currentDevice: String

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  83. def currentDeviceFunction: (ops.OpSpecification) ⇒ String

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  84. def currentNameScope: String

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  85. def currentVariableScope: ops.variables.VariableScope

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  86. def device[R](device: String)(block: ⇒ R): R

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  87. def deviceFunction[R](deviceFunction: (ops.OpSpecification) ⇒ String)(block: ⇒ R): R

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  88. final def eq(arg0: AnyRef): Boolean

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  89. def equals(arg0: Any): Boolean

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  90. def finalize(): Unit

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  91. final def getClass(): Class[_]

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  92. def hashCode(): Int

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  93. final def isInstanceOf[T0]: Boolean

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  94. def nameScope[R](nameScope: String)(block: ⇒ R): R

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  95. final def ne(arg0: AnyRef): Boolean

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  96. final def notify(): Unit

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  97. final def notifyAll(): Unit

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  98. final def synchronized[T0](arg0: ⇒ T0): T0

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  99. def toString(): String

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  100. def updatedVariableScope[R](variableScope: ops.variables.VariableScope, reuse: ReuseAllowed = ReuseOrCreateNew, dataType: types.DataType = null, initializer: Initializer = null, regularizer: Regularizer = null, partitioner: Partitioner = null, cachingDevice: (ops.OpSpecification) ⇒ String = null, customGetter: ops.variables.Variable.VariableGetter = null, isPure: Boolean = false)(block: ⇒ R): R

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  101. def variableScope[R](name: String, reuse: ReuseAllowed = ReuseOrCreateNew, dataType: types.DataType = null, initializer: Initializer = null, regularizer: Regularizer = null, partitioner: Partitioner = null, cachingDevice: (ops.OpSpecification) ⇒ String = null, customGetter: ops.variables.Variable.VariableGetter = null, isDefaultName: Boolean = false, isPure: Boolean = false)(block: ⇒ R): R

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  102. final def wait(): Unit

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  103. final def wait(arg0: Long, arg1: Int): Unit

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  104. final def wait(arg0: Long): Unit

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