Make.Neuronmodule Optimise : Owl_optimise_generic_sig.Sigmodule Init : sig ... endmodule Input : sig ... endmodule Activation : sig ... endmodule Linear : sig ... endmodule LinearNoBias : sig ... endmodule Recurrent : sig ... endmodule LSTM : sig ... endmodule GRU : sig ... endmodule Conv1D : sig ... endmodule Conv2D : sig ... endmodule Conv3D : sig ... endmodule DilatedConv1D : sig ... endmodule DilatedConv2D : sig ... endmodule DilatedConv3D : sig ... endmodule TransposeConv1D : sig ... endmodule TransposeConv2D : sig ... endmodule TransposeConv3D : sig ... endmodule FullyConnected : sig ... endmodule MaxPool1D : sig ... endmodule MaxPool2D : sig ... endmodule AvgPool1D : sig ... endmodule AvgPool2D : sig ... endmodule GlobalMaxPool1D : sig ... endmodule GlobalMaxPool2D : sig ... endmodule GlobalAvgPool1D : sig ... endmodule GlobalAvgPool2D : sig ... endmodule UpSampling1D : sig ... endmodule UpSampling2D : sig ... endmodule UpSampling3D : sig ... endmodule Padding1D : sig ... endmodule Padding2D : sig ... endmodule Padding3D : sig ... endmodule Lambda : sig ... endmodule LambdaArray : sig ... endmodule Dropout : sig ... endmodule Reshape : sig ... endmodule Flatten : sig ... endmodule Slice : sig ... endmodule Add : sig ... endmodule Mul : sig ... endmodule Dot : sig ... endmodule Max : sig ... endmodule Average : sig ... endmodule Concatenate : sig ... endmodule Normalisation : sig ... endmodule GaussianNoise : sig ... endmodule GaussianDropout : sig ... endmodule AlphaDropout : sig ... endmodule Embedding : sig ... endmodule Masking : sig ... endtype neuron = | Input of Input.neuron_typ| Linear of Linear.neuron_typ| LinearNoBias of LinearNoBias.neuron_typ| Embedding of Embedding.neuron_typ| LSTM of LSTM.neuron_typ| GRU of GRU.neuron_typ| Recurrent of Recurrent.neuron_typ| Conv1D of Conv1D.neuron_typ| Conv2D of Conv2D.neuron_typ| Conv3D of Conv3D.neuron_typ| DilatedConv1D of DilatedConv1D.neuron_typ| DilatedConv2D of DilatedConv2D.neuron_typ| DilatedConv3D of DilatedConv3D.neuron_typ| TransposeConv1D of TransposeConv1D.neuron_typ| TransposeConv2D of TransposeConv2D.neuron_typ| TransposeConv3D of TransposeConv3D.neuron_typ| FullyConnected of FullyConnected.neuron_typ| MaxPool1D of MaxPool1D.neuron_typ| MaxPool2D of MaxPool2D.neuron_typ| AvgPool1D of AvgPool1D.neuron_typ| AvgPool2D of AvgPool2D.neuron_typ| GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ| GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ| GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ| GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ| UpSampling2D of UpSampling2D.neuron_typ| Padding2D of Padding2D.neuron_typ| Dropout of Dropout.neuron_typ| Reshape of Reshape.neuron_typ| Flatten of Flatten.neuron_typ| Slice of Slice.neuron_typ| Lambda of Lambda.neuron_typ| LambdaArray of LambdaArray.neuron_typ| Activation of Activation.neuron_typ| GaussianNoise of GaussianNoise.neuron_typ| GaussianDropout of GaussianDropout.neuron_typ| AlphaDropout of AlphaDropout.neuron_typ| Normalisation of Normalisation.neuron_typ| Add of Add.neuron_typ| Mul of Mul.neuron_typ| Dot of Dot.neuron_typ| Max of Max.neuron_typ| Average of Average.neuron_typ| Concatenate of Concatenate.neuron_typTypes of neuron.
*)val get_in_out_shape : neuron -> int array * int arrayGet both input and output shapes of a neuron.
val get_in_shape : neuron -> int arrayGet the input shape of a neuron.
val get_out_shape : neuron -> int arrayGet the output shape of a neuron.
val connect : int array array -> neuron -> unitConnect this neuron to others in a neural network.
val init : neuron -> unitInitialise the neuron and its parameters.
val reset : neuron -> unitReset the parameters in a neuron.
val mktag : int -> neuron -> unitTag the neuron, used by Algodiff module.
val mkpar : neuron -> Optimise.Algodiff.t arrayAssemble all the trainable parameters in an array, used by Optimise module.
val mkpri : neuron -> Optimise.Algodiff.t arrayAssemble all the primal values in an array, used by Optimise module.
val mkadj : neuron -> Optimise.Algodiff.t arrayAssemble all the adjacent values in an array, used by Optimise module.
val update : neuron -> Optimise.Algodiff.t array -> unitUpdate trainable parameters in a neuron, used by Optimise module.
val load_weights : neuron -> Optimise.Algodiff.t array -> unitLoad both trainable and non-trainable parameters into the neuron.
val save_weights : neuron -> Optimise.Algodiff.t arrayAssemble both trainable and non-trainable parameters of the neuron.
val run : Optimise.Algodiff.t array -> neuron -> Optimise.Algodiff.tExecute the computation in this neuron.
val to_string : neuron -> stringConvert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.
val to_name : neuron -> stringReturn the name of the neuron.