package caisar

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in-package search v0.2.0

A platform for characterizing the safety and robustness of artificial intelligence based software

Install

sha256=58ba1e38721795b306c860b56aaeba971be586cd55fb96e3ec8af72dd005101b

sha512=f1b3b9899660745598cebe7ecb52a39e9e16dcb7352381ea75a80d2afa988437130c00bf66355991421d4cb3dc06b02c185f7d4bdcc1c86dfcde8084bd01a654

Module to parse neural networks written in the NNet format (see the specification at https://github.com/sisl/NNet).

Sourcetype t = private {

n_layers : int;
(*
Number of layers.
*)
n_inputs : int;
(*
Number of inputs.
*)
n_outputs : int;
(*
Number of outputs.
*)
max_layer_size : int;
(*
Maximum layer size.
*)
layer_sizes : int list;
(*
Size of each layer.
*)
min_input_values : float list option;
(*
Minimum values of inputs.
*)
max_input_values : float list option;
(*
Maximum values of inputs.
*)
mean_values : (float list * float) option;
(*
Mean values of inputs and one value for all outputs.
*)
range_values : (float list * float) option;
(*
Range values of inputs and one value for all outputs.
*)
weights_biases : float list list;
(*
All weights and biases of NNet model.
*)
nir : Nir.Ngraph.t;

}

NNet model metadata.

Sourceval parse : ?permissive:bool -> string -> (t, string) Stdlib.Result.t

Parse an NNet file.

parameter permissive
false by default. When set, parsing does not fail on non available information, which are set to None instead.

Sourceval to_nir : t -> Nir.Ngraph.t

Convert a well-formed NNet into a Nir.