keras

Type Members

1. class Activation[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

   Simple activation function to be applied to the output.

2. class AtrousConvolution1D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

   Applies an atrous convolution operator for filtering neighborhoods of 1-D inputs.

3. class AtrousConvolution2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

   Applies an atrous convolution operator for filtering windows of 2-D inputs.

4. class AveragePooling1D[T] extends Pooling1D[T]

   Applies average pooling operation for temporal data.

5. class AveragePooling2D[T] extends Pooling2D[T]

   Applies average pooling operation for spatial data.

6. class AveragePooling3D[T] extends Pooling3D[T]

   Applies average pooling operation for 3D data (spatial or spatio-temporal).

7. class BatchNormalization[T] extends KerasLayer[Tensor[T], Tensor[T], T]

   Batch normalization layer.

8. class Bidirectional[T] extends KerasLayer[Tensor[T], Tensor[T], T]

   Bidirectional wrapper for RNNs.

9. class ConvLSTM2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

   Convolutional LSTM.

10. class Convolution1D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Applies convolution operator for filtering neighborhoods of 1-D inputs.

11. class Convolution2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Applies a 2D convolution over an input image composed of several input planes.

12. class Convolution3D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Applies convolution operator for filtering windows of three-dimensional inputs.

13. class Cropping1D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Cropping layer for 1D input (e.

14. class Cropping2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Cropping layer for 2D input (e.

15. class Cropping3D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Cropping layer for 3D data (e.

16. class Deconvolution2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Transposed convolution operator for filtering windows of 2-D inputs.

17. class Dense[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    A densely-connected NN layer.

18. class Dropout[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    Applies Dropout to the input by randomly setting a fraction 'p' of input units to 0 at each update during training time in order to prevent overfitting.

19. class ELU[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    Exponential Linear Unit.

20. class Embedding[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Turn positive integers (indexes) into dense vectors of fixed size.

21. class Flatten[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Flattens the input without affecting the batch size.

22. class GRU[T] extends Recurrent[T]

    Gated Recurrent Unit architecture.

23. class GaussianDropout[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    Apply multiplicative 1-centered Gaussian noise.

24. class GaussianNoise[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    Apply additive zero-centered Gaussian noise.

25. class GlobalAveragePooling1D[T] extends GlobalPooling1D[T]

    Applies global average pooling operation for temporal data.

26. class GlobalAveragePooling2D[T] extends GlobalPooling2D[T]

    Applies global average pooling operation for spatial data.

27. class GlobalAveragePooling3D[T] extends GlobalPooling3D[T]

    Applies global average pooling operation for 3D data.

28. class GlobalMaxPooling1D[T] extends GlobalPooling1D[T]

    Applies global max pooling operation for temporal data.

29. class GlobalMaxPooling2D[T] extends GlobalPooling2D[T]

    Applies global max pooling operation for spatial data.

30. class GlobalMaxPooling3D[T] extends GlobalPooling3D[T]

    Applies global max pooling operation for 3D data.

31. abstract class GlobalPooling1D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Abstract class for different global pooling 1D layers.

32. abstract class GlobalPooling2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Abstract class for different global pooling 2D layers.

33. abstract class GlobalPooling3D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Abstract class for different global pooling 3D layers.

34. class Highway[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Densely connected highway network.

35. class Input[T] extends KerasLayer[Activity, Activity, T]

36. class KerasIdentityWrapper[T] extends KerasLayer[Activity, Activity, T]

    Wrap a torch style layer to keras style layer.

37. abstract class KerasLayer[A <: Activity, B <: Activity, T] extends Container[A, B, T]

    KerasModule is the basic component of all Keras-like Layer.

38. trait KerasLayerSerializable extends ContainerSerializable with TKerasSerializerHelper

39. class KerasLayerWrapper[T] extends KerasLayer[Activity, Activity, T]

    Wrap a torch style layer to keras style layer.

40. abstract class KerasModel[T] extends KerasLayer[Activity, Activity, T]

41. class LSTM[T] extends Recurrent[T]

    Long Short Term Memory unit architecture.

42. class LeakyReLU[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    Leaky version of a Rectified Linear Unit.

43. class LocallyConnected1D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Locally-connected layer for 1D inputs which works similarly to the TemporalConvolution layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

44. class LocallyConnected2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Locally-connected layer for 2D inputs that works similarly to the SpatialConvolution layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

45. class Masking[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    Use a mask value to skip timesteps for a sequence.

46. class MaxPooling1D[T] extends Pooling1D[T]

    Applies max pooling operation for temporal data.

47. class MaxPooling2D[T] extends Pooling2D[T]

    Applies max pooling operation for spatial data.

48. class MaxPooling3D[T] extends Pooling3D[T]

    Applies max pooling operation for 3D data (spatial or spatio-temporal).

49. class MaxoutDense[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    A dense maxout layer that takes the element-wise maximum of linear layers.

50. class Merge[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Used to merge a list of inputs into a single output, following some merge mode.

51. class Model[T] extends KerasModel[T]

52. class Permute[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Permutes the dimensions of the input according to a given pattern.

53. abstract class Pooling1D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Abstract class for different pooling 1D layers.

54. abstract class Pooling2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Abstract class for different pooling 2D layers.

55. abstract class Pooling3D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Abstract class for different pooling 3D layers.

56. abstract class Recurrent[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    This is the abstract base class for recurrent layers.

57. class RepeatVector[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Repeats the input n times.

58. class Reshape[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Reshapes an output to a certain shape.

59. class SReLU[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    S-shaped Rectified Linear Unit.

60. class SeparableConvolution2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Applies separable convolution operator for 2D inputs.

61. class Sequential[T] extends KerasModel[T]

62. class SimpleRNN[T] extends Recurrent[T]

    A fully-connected recurrent neural network cell.

63. class SoftMax[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Just a wrapper class.

64. class SpatialDropout1D[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    Spatial 1D version of Dropout.

65. class SpatialDropout2D[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    Spatial 2D version of Dropout.

66. class SpatialDropout3D[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    Spatial 3D version of Dropout.

67. class ThresholdedReLU[T] extends KerasLayer[Tensor[T], Tensor[T], T] with IdentityOutputShape

    Thresholded Rectified Linear Unit.

68. class TimeDistributed[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    TimeDistributed wrapper.

69. class UpSampling1D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    UpSampling layer for 1D inputs.

70. class UpSampling2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    UpSampling layer for 2D inputs.

71. class UpSampling3D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    UpSampling layer for 3D inputs.

72. class ZeroPadding1D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Zero-padding layer for 1D input (e.

73. class ZeroPadding2D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Zero-padding layer for 2D input (e.

74. class ZeroPadding3D[T] extends KerasLayer[Tensor[T], Tensor[T], T]

    Zero-padding layer for 3D data (spatial or spatio-temporal).