gluonts.mx.block.snmlp module#

class gluonts.mx.block.snmlp.SNMLPBlock(in_units: int, hidden_units: int, out_units: int, num_hidden_layers: int = 2, activation: str = 'lipswish', jacobian_method: str = 'bf', num_power_iter: int = 1, coeff: float = 0.9, flatten: bool = False)[source]#

Bases: mxnet.gluon.block.HybridBlock

get_weights()[source]#

hybrid_forward(F, x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]#

Parameters: x – Input Tensor
Returns: output of SNMLPBlock
Return type: Tensor

jacobian(x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]#

Parameters: x – Input Tensor
Returns: Jacobian of the SNMLPBlock evaluated at x.
Return type: Tensor

gluonts.mx.block.snmlp.jacobian_sn_mlp_block_bf(layers: List[Tuple[mxnet.gluon.block.HybridBlock, Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]]]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]#

Brute force computation of the jacobian of a SNMlpBlock jac is of shape (Batch dim1, …, Output dim, Input dim)

Parameters: layers – A list of tuples where each tuple (layer, input) is associated to a composing layer of the SNMLPBlock, where layer corresponds to the associated object layer, along with its input tensor.
Returns: Jacobian of the SNMLPBlock computed at a given input
Return type: Tensor