diff --git a/lib/nnet/dune b/lib/nnet/dune
index c15dbf7657c947f6e8612b6eebc43618a7526576..fb2c5ec74be4294d311033bf92eed89bf913e5da 100644
--- a/lib/nnet/dune
+++ b/lib/nnet/dune
@@ -1,5 +1,5 @@
(library
(name nnet)
(public_name nnet)
- (libraries base)
+ (libraries base csv)
(synopsis "NNet parser"))
diff --git a/lib/nnet/nnet.ml b/lib/nnet/nnet.ml
index be5bf793b3cabcd1df24a4c263ecb8cea5e02beb..7429c0a53ba17734469d23e2939a1dcb580a8b61 100644
--- a/lib/nnet/nnet.ml
+++ b/lib/nnet/nnet.ml
@@ -19,25 +19,23 @@ type t = {
max_input_values : float list;
mean_values : float list * float;
range_values : float list * float;
+ weights_biases : float list list;
}
-[@@deriving show { with_path = false }]
(* NNet format handling. *)
let nnet_format_error s =
Error (Format.sprintf "NNet format error: %s condition not satisfied." s)
-let nnet_delimiter = Str.regexp ","
-
-(* Parse a single NNet format line: split line using [nnet_delimiter] as
- delimiter, and convert each string into a number by means of converter [f]. *)
-let handle_nnet_line ~f line =
+(* Parse a single NNet format line: split line wrt CSV format, and convert each
+ string into a number by means of converter [f]. *)
+let handle_nnet_line ~f in_channel =
List.filter_map
~f:(fun s -> try Some (f (String.strip s)) with _ -> None)
- (Str.split nnet_delimiter line)
+ (Csv.next in_channel)
(* Skip the header part, ie comments, of the NNet format. *)
-let handle_nnet_header filename in_channel =
+let skip_nnet_header filename in_channel =
let exception End_of_header in
let pos_in = ref (Stdlib.pos_in in_channel) in
try
@@ -58,19 +56,16 @@ let handle_nnet_header filename in_channel =
(* Retrieve number of layers, inputs, outputs and maximum layer size. *)
let handle_nnet_basic_info in_channel =
- try
- let line = Stdlib.input_line in_channel in
- match handle_nnet_line ~f:Stdlib.int_of_string line with
- | [ n_layers; n_inputs; n_outputs; max_layer_size ] ->
- Ok (n_layers, n_inputs, n_outputs, max_layer_size)
- | _ -> nnet_format_error "second"
- with End_of_file -> nnet_format_error "second"
+ match handle_nnet_line ~f:Int.of_string in_channel with
+ | [ n_layers; n_inputs; n_outputs; max_layer_size ] ->
+ Ok (n_layers, n_inputs, n_outputs, max_layer_size)
+ | _ -> nnet_format_error "second"
+ | exception End_of_file -> nnet_format_error "second"
(* Retrieve size of each layer, including inputs and outputs. *)
let handle_nnet_layer_sizes n_layers in_channel =
try
- let line = Stdlib.input_line in_channel in
- let layer_sizes = handle_nnet_line ~f:Stdlib.int_of_string line in
+ let layer_sizes = handle_nnet_line ~f:Int.of_string in_channel in
if List.length layer_sizes = n_layers + 1 then Ok layer_sizes
else nnet_format_error "third"
with End_of_file -> nnet_format_error "third"
@@ -78,15 +73,14 @@ let handle_nnet_layer_sizes n_layers in_channel =
(* Skip unused flag. *)
let handle_nnet_unused_flag in_channel =
try
- let _ = Stdlib.input_line in_channel in
+ let _ = Csv.next in_channel in
Ok ()
with End_of_file -> nnet_format_error "forth"
(* Retrive minimum values of inputs. *)
let handle_nnet_min_input_values n_inputs in_channel =
try
- let line = Stdlib.input_line in_channel in
- let min_input_values = handle_nnet_line ~f:Stdlib.float_of_string line in
+ let min_input_values = handle_nnet_line ~f:Float.of_string in_channel in
if List.length min_input_values = n_inputs then Ok min_input_values
else nnet_format_error "fifth"
with End_of_file -> nnet_format_error "fifth"
@@ -94,8 +88,7 @@ let handle_nnet_min_input_values n_inputs in_channel =
(* Retrive maximum values of inputs. *)
let handle_nnet_max_input_values n_inputs in_channel =
try
- let line = Stdlib.input_line in_channel in
- let max_input_values = handle_nnet_line ~f:Stdlib.float_of_string line in
+ let max_input_values = handle_nnet_line ~f:Float.of_string in_channel in
if List.length max_input_values = n_inputs then Ok max_input_values
else nnet_format_error "sixth"
with End_of_file -> nnet_format_error "sixth"
@@ -103,8 +96,7 @@ let handle_nnet_max_input_values n_inputs in_channel =
(* Retrieve mean values of inputs and one value for all outputs. *)
let handle_nnet_mean_values n_inputs in_channel =
try
- let line = Stdlib.input_line in_channel in
- let mean_values = handle_nnet_line ~f:Stdlib.float_of_string line in
+ let mean_values = handle_nnet_line ~f:Float.of_string in_channel in
if List.length mean_values = n_inputs + 1 then
let mean_input_values, mean_output_value =
List.split_n mean_values n_inputs
@@ -116,8 +108,7 @@ let handle_nnet_mean_values n_inputs in_channel =
(* Retrieve range values of inputs and one value for all outputs. *)
let handle_nnet_range_values n_inputs in_channel =
try
- let line = Stdlib.input_line in_channel in
- let range_values = handle_nnet_line ~f:Stdlib.float_of_string line in
+ let range_values = handle_nnet_line ~f:Float.of_string in_channel in
if List.length range_values = n_inputs + 1 then
let range_input_values, range_output_value =
List.split_n range_values n_inputs
@@ -126,13 +117,27 @@ let handle_nnet_range_values n_inputs in_channel =
else nnet_format_error "eighth"
with End_of_file -> nnet_format_error "eighth"
-(* Retrieves [filename] NNet model metadata wrt NNet format specification (see
- https://github.com/sisl/NNet for details.) *)
-let parse_metadata filename =
+(* Retrieve all layer weights and biases as appearing in the model. No special
+ treatment is performed. *)
+let handle_nnet_weights_and_biases in_channel =
+ List.rev
+ (Csv.fold_left ~init:[]
+ ~f:(fun fll sl ->
+ List.filter_map
+ ~f:(fun s ->
+ try Some (Float.of_string (String.strip s)) with _ -> None)
+ sl
+ :: fll)
+ in_channel)
+
+(* Retrieves [filename] NNet model metadata and weights wrt NNet format
+ specification (see https://github.com/sisl/NNet for details). *)
+let parse filename =
let open Result in
- let in_channel = Stdlib.open_in filename in
try
- handle_nnet_header filename in_channel >>= fun () ->
+ let in_channel = Stdlib.open_in filename in
+ skip_nnet_header filename in_channel >>= fun () ->
+ let in_channel = Csv.of_channel in_channel in
handle_nnet_basic_info in_channel >>= fun (n_ls, n_is, n_os, max_l_size) ->
handle_nnet_layer_sizes n_ls in_channel >>= fun layer_sizes ->
handle_nnet_unused_flag in_channel >>= fun () ->
@@ -140,7 +145,8 @@ let parse_metadata filename =
handle_nnet_max_input_values n_is in_channel >>= fun max_input_values ->
handle_nnet_mean_values n_is in_channel >>= fun mean_values ->
handle_nnet_range_values n_is in_channel >>= fun range_values ->
- Stdlib.close_in in_channel;
+ let weights_biases = handle_nnet_weights_and_biases in_channel in
+ Csv.close_in in_channel;
Ok
{
n_layers = n_ls;
@@ -152,5 +158,9 @@ let parse_metadata filename =
max_input_values;
mean_values;
range_values;
+ weights_biases;
}
- with Failure msg -> Error (Format.sprintf "Unexpected error: %s." msg)
+ with
+ | Csv.Failure (_nrecord, _nfield, msg) -> Error msg
+ | Sys_error s -> Error s
+ | Failure msg -> Error (Format.sprintf "Unexpected error: %s." msg)
diff --git a/lib/nnet/nnet.mli b/lib/nnet/nnet.mli
index b718de82b47e212ad65103995899a915c8a4be7d..a1b5076fefaccfcea806f896708b3d48260d0eeb 100644
--- a/lib/nnet/nnet.mli
+++ b/lib/nnet/nnet.mli
@@ -16,9 +16,9 @@ type t = private {
(** Mean values of inputs and one value for all outputs. *)
range_values : float list * float;
(** Range values of inputs and one value for all outputs. *)
+ weights_biases : float list list; (** All weights and biases of NNet model. *)
}
-[@@deriving show { with_path = false }]
(** NNet model metadata. *)
-val parse_metadata : string -> (t, string) Result.t
-(** Parse an NNet file for metadata. *)
+val parse : string -> (t, string) Result.t
+(** Parse an NNet file. *)
diff --git a/src/dune b/src/dune
index 0c6fcbae294537ccc076196fef95ca4b981204b5..14cf47c969863f82832fa57606230984451721b6 100644
--- a/src/dune
+++ b/src/dune
@@ -2,6 +2,6 @@
(name main)
(public_name caisar)
(libraries menhirLib yojson cmdliner logs logs.cli logs.fmt fmt.tty base unix str ppx_deriving_yojson.runtime nnet why3)
- (preprocess (pps ppx_deriving_yojson ppx_deriving.show ppx_deriving.ord ppx_deriving.eq))
- (package caisar)
+ (preprocess (pps ppx_deriving_yojson ppx_deriving.show ppx_deriving.ord ppx_deriving.eq))
+ (package caisar)
)