ab.hlayers¶
Higherorder neural network layers (made from other layers).

class
aboleth.hlayers.
Concat
(*layers)¶ Bases:
aboleth.baselayers.MultiLayer
Concatenates the output of multiple layers.
Parameters: layers ([MultiLayer]) – The layers to concatenate. 
__call__
(**kwargs)¶ Construct the subgraph for this layer.
Parameters: **kwargs – the inputs to this layer (Tensors) Returns:  Net (Tensor) – the output of this layer
 KL (float, Tensor) – the regularizer/Kullback Leibler ‘cost’ of the parameters in this layer.


class
aboleth.hlayers.
PerFeature
(*layers, slices=None)¶ Bases:
aboleth.baselayers.Layer
Concatenate multiple layers with sliced inputs.
Each layer will recieve a slice along the last axis of the input to the new function. In other words,
PerFeature(l1, l2)(X)
will calll1(X[..., 0]) and l2(X[..., 1])
then concatenate their outputs into a single tensor. This is mostly useful for simplifying embedding multiple categorical inputs that are stored columnwise in the same 2D tensor.This function assumes the tensor being provided is 3D.
Parameters:  layers ([Layer]) – The layers to concatenate.
 slices ([slice]) – The slices into X to give to each layer, this has to be the same length as layers. If this is None, it will give columns of X to each layer, the number of columns is determined by the number of layers.

__call__
(X)¶ Construct the subgraph for this layer.
Parameters: X (Tensor) – the input to this layer Returns:  Net (Tensor) – the output of this layer
 KL (float, Tensor) – the regularizer/Kullback Leibler ‘cost’ of the parameters in this layer.

class
aboleth.hlayers.
Sum
(*layers)¶ Bases:
aboleth.baselayers.MultiLayer
Sums multiple layers by adding their outputs.
Parameters: layers ([MultiLayer]) – The layers to add. 
__call__
(**kwargs)¶ Construct the subgraph for this layer.
Parameters: **kwargs – the inputs to this layer (Tensors) Returns:  Net (Tensor) – the output of this layer
 KL (float, Tensor) – the regularizer/Kullback Leibler ‘cost’ of the parameters in this layer.
