Revise examples (#672)

Keep examples up-to-date with current version of Owl/OCaml.

examples/README.md

@@ -1 +1 @@
-The examples in this folder are made based on the OCaml's toplevel and Owl's Zoo system. Please refer to [Tutorial 9: Scripting and Zoo System](https://github.com/ryanrhymes/owl/wiki/Tutorial:-Scripting-and-Zoo-System).
+The examples can be compiled using `dune build`, and then the executables can be found in `owl/_build/default/examples`.

examples/backprop.ml

@@ -1,9 +1,11 @@
-#!/usr/bin/env owl
 (* This example demonstrates how to write the backpropogation algorithm from
    scratch using Algodiff module. With the backprop algorithm, we further make
    a naive neural network without using Owl' DNN to train on mnist dataset.
+
+   Execute 'Dataset.download_all ()' to accquire all necessary dataset before running this example.'
  *)
 
+
 open Owl
 
 open Algodiff.S
@@ -49,7 +51,7 @@ let backprop nn eta x y =
   loss |> unpack_flt
 
 let test nn x y =
-  Dense.Matrix.S.iter2_rows (fun u v ->
+  Dense.Matrix.S.iter2_rows (fun u _ ->
     Dataset.print_mnist_image u;
     let p = run_network (Arr u) nn |> unpack_arr in
     Dense.Matrix.Generic.print p;

examples/checkpoint.ml

@@ -1,4 +1,3 @@
-#!/usr/bin/env owl
 (* This example shows how to use checkpoint in a stateful optimisation. *)
 
 open Owl

examples/cifar10_vgg.ml

@@ -1,4 +1,3 @@
-#!/usr/bin/env owl
 (* This example demonstrates how to build a VGG-like convolutional neural
  * network for CIFAR10 dataset.
  *)

examples/computation_graph_01.ml

@@ -1,6 +1,9 @@
-#!/usr/bin/env owl
+(*
+ * Please install the graphvis tool before executing this example. 
+ E.g. on Ubuntu system: `sudo apt install graphviz`
+*)
+
 
-open Owl
 module G = Owl_computation_cpu_engine.Make (Owl_algodiff_primal_ops.D)
 include Owl_algodiff_generic.Make (G)
 
@@ -14,7 +17,7 @@ let visualise_01 () =
   let z = f x y in
   let inputs = [| unpack_elt x |> G.elt_to_node; unpack_elt y |> G.elt_to_node |] in
   let outputs = [| unpack_elt z |> G.elt_to_node |] in
-  let graph = G.make_graph inputs outputs "graph" in
+  let graph = G.make_graph ~input:inputs ~output:outputs "graph" in
   let s = G.graph_to_dot graph in
   Owl_io.write_file "cgraph_01.dot" s;
   Sys.command "dot -Tpdf cgraph_01.dot -o cgraph_01.pdf" |> ignore
@@ -26,7 +29,7 @@ let visualise_02 () =
   let z = (grad (f x)) y in
   let inputs = [| unpack_elt x |> G.elt_to_node; unpack_elt y |> G.elt_to_node |] in
   let outputs = [| unpack_elt z |> G.elt_to_node |] in
-  let s = G.make_graph inputs outputs "graph" |> G.graph_to_dot in
+  let s = G.make_graph ~input:inputs ~output:outputs "graph" |> G.graph_to_dot in
   Owl_io.write_file "cgraph_02.dot" s;
   Sys.command "dot -Tpdf cgraph_02.dot -o cgraph_02.pdf" |> ignore
 
@@ -38,18 +41,18 @@ let visualise_03 () =
   let z = f x y in
   let i0 = [| unpack_arr x |> G.arr_to_node; unpack_arr y |> G.arr_to_node |] in
   let o0 = [| primal z |> unpack_elt |> G.elt_to_node |] in
-  let s0 = G.make_graph i0 o0 "graph" |> G.graph_to_dot in
+  let s0 = G.make_graph ~input:i0 ~output:o0 "graph" |> G.graph_to_dot in
   Owl_io.write_file "cgraph_03_forward.dot" s0;
   Sys.command "dot -Tpdf cgraph_03_forward.dot -o cgraph_03_forward.pdf" |> ignore;
 
   reverse_prop (pack_flt 1.) z;
   let x' = adjval x |> unpack_arr |> G.arr_to_node in
   let y' = adjval y |> unpack_arr |> G.arr_to_node in
   let i1 = [| unpack_arr x |> G.arr_to_node |] in
-  let s1 = G.make_graph i1 [| x' |] "graph" |> G.graph_to_dot in
+  let s1 = G.make_graph ~input:i1 ~output:[| x' |] "graph" |> G.graph_to_dot in
   let i2 = [| unpack_arr y |> G.arr_to_node |] in
-  let s2 = G.make_graph i2 [| y' |] "graph" |> G.graph_to_dot in
-  let s3 = G.make_graph i0 [| x'; y' |] "graph" |> G.graph_to_dot in
+  let s2 = G.make_graph ~input:i2 ~output:[| y' |] "graph" |> G.graph_to_dot in
+  let s3 = G.make_graph ~input:i0 ~output:[| x'; y' |] "graph" |> G.graph_to_dot in
   Owl_io.write_file "cgraph_03_backward_x.dot" s1;
   Sys.command "dot -Tpdf cgraph_03_backward_x.dot -o cgraph_03_backward_x.pdf" |> ignore;
   Owl_io.write_file "cgraph_03_backward_y.dot" s2;

examples/computation_graph_02.ml

@@ -1,4 +1,8 @@
-#!/usr/bin/env owl
+(*
+ * Please install the graphvis tool before executing this example. 
+ E.g. on Ubuntu system: `sudo apt install graphviz`
+*)
+
 
 open Owl
 module G = Owl_computation_cpu_engine.Make (Owl_algodiff_primal_ops.D)
@@ -32,14 +36,14 @@ let visualise_mnist () =
   let _, adj0 = Graph.(backward network loss) in
   let inputs = [| xt |> A.unpack_arr |> G.arr_to_node |] in
   let s0_outputs = [| loss |> A.unpack_elt |> G.elt_to_node |] in
-  let s0 = G.make_graph inputs s0_outputs "mnist_loss" |> G.graph_to_dot in
+  let s0 = G.make_graph ~input:inputs ~output:s0_outputs "mnist_loss" |> G.graph_to_dot in
   Owl_io.write_file "cgraph_04_mnist_loss.dot" s0;
   Sys.command "dot -Tpdf cgraph_04_mnist_loss.dot -o cgraph_04_mnist_loss.pdf" |> ignore;
   let s1_outputs = adj0 
     |> Utils.Array.flatten
     |> Array.map (fun a -> A.unpack_arr a |> G.arr_to_node)
   in
-  let s1 = G.make_graph inputs s1_outputs "mnist_loss" |> G.graph_to_dot in
+  let s1 = G.make_graph ~input:inputs ~output:s1_outputs "mnist_loss" |> G.graph_to_dot in
   Owl_io.write_file "cgraph_04_mnist_grad.dot" s1;
   Sys.command "dot -Tpdf cgraph_04_mnist_grad.dot -o cgraph_04_mnist_grad.pdf" |> ignore
 
@@ -63,14 +67,14 @@ let visualise_lstm () =
   let _, adj0 = Graph.(backward network loss) in
   let inputs = [| xt |> A.unpack_arr |> G.arr_to_node |] in
   let s0_outputs = [| loss |> A.unpack_elt |> G.elt_to_node |] in
-  let s0 = G.make_graph inputs s0_outputs "mnist_loss" |> G.graph_to_dot in
+  let s0 = G.make_graph ~input:inputs ~output:s0_outputs "mnist_loss" |> G.graph_to_dot in
   Owl_io.write_file "cgraph_04_lstm_loss.dot" s0;
   (* Sys.command "dot -Tpdf -Gnslimit=1 cgraph_04_lstm_loss.dot -o cgraph_04_lstm_loss.pdf" |> ignore; *)
   let s1_outputs = adj0 
     |> Utils.Array.flatten
     |> Array.map (fun a -> A.unpack_arr a |> G.arr_to_node)
   in
-  let s1 = G.make_graph inputs s1_outputs "mnist_loss" |> G.graph_to_dot in
+  let s1 = G.make_graph ~input:inputs ~output:s1_outputs "mnist_loss" |> G.graph_to_dot in
   Owl_io.write_file "cgraph_04_lstm_grad.dot" s1
   (* Sys.command "dot -Tpdf -Gnslimit=1 cgraph_04_lstm_grad.dot -o cgraph_04_lstm_grad.pdf" |> ignore *)
 

examples/countmin_distributed.ml

@@ -1,4 +1,3 @@
-#!/usr/bin/env owl
 (* This example demonstrates the use of distributed count-min sketches. It
  * fills a single count-min sketch using the news.txt corpus at 
  * https://github.com/ryanrhymes/owl_dataset, then initializes two new empty

examples/countmin_texts.ml

@@ -1,4 +1,3 @@
-#!/usr/bin/env owl
 (* This example demonstrates the use of the HeavyHitters sketch,
  * which is based on the Count-Min sketch in Owl_base library.  
  * This example finds the words which appear with relative frequency

examples/custom_algodiff_op.ml

@@ -1,5 +1,3 @@
-#!/usr/bin/env owl
-
 (* This example demonstrates how to build a custom operation in Algodiff . *)
 
 open Owl
@@ -33,13 +31,13 @@ let custom_cos =
         : Siso)
 
 let _ =
-  let input = Mat.uniform 1 2 in
+  let input = _f 1. in (* Mat.uniform 1 2 in *)
   (* [f] must be [f : vector -> scalar]. *)
-  let g' = grad custom_cos in
-  let h' = grad g' in
-  let g = grad Maths.cos in
-  let h = grad g in
-  Mat.print (g' input);
-  Mat.print (g input);
-  Mat.print (h' input);
-  Mat.print (h input);
+  let g' = diff custom_cos in
+  let h' = diff g' in
+  let g = diff Maths.cos in
+  let h = diff g in
+  print_float (g' input |> unpack_flt); print_endline "\n";
+  print_float (g input  |> unpack_flt); print_endline "\n";
+  print_float (h' input |> unpack_flt); print_endline "\n";
+  print_float (h input  |> unpack_flt); print_endline "\n"