From: François Fleuret <francois@fleuret.org>
Date: Sun, 2 Jul 2023 16:34:10 +0000 (+0200)
Subject: Update.
X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=4502a109727b0424ff6d4df90f17b361524f9e73;p=picoclvr.git

Update.
---

diff --git a/main.py b/main.py
index 314a961..bb1e7b4 100755
--- a/main.py
+++ b/main.py
@@ -113,7 +113,9 @@ parser.add_argument("--stack_nb_steps", type=int, default=100)
 
 parser.add_argument("--stack_nb_stacks", type=int, default=1)
 
-parser.add_argument("--stack_nb_digits", type=int, default=1)
+parser.add_argument("--stack_nb_digits", type=int, default=3)
+
+parser.add_argument("--stack_fraction_values_for_train", type=float, default=None)
 
 ######################################################################
 
@@ -876,6 +878,7 @@ class TaskStack(Task):
         nb_steps,
         nb_stacks,
         nb_digits,
+        fraction_values_for_train=None,
         device=torch.device("cpu"),
     ):
         self.batch_size = batch_size
@@ -884,12 +887,31 @@ class TaskStack(Task):
         self.nb_digits = nb_digits
         self.device = device
 
+        if fraction_values_for_train is None:
+            values_for_train = None
+            values_for_test = None
+        else:
+            all = torch.randperm(10**nb_digits)
+            nb_for_train = int(all.size(0) * fraction_values_for_train)
+            values_for_train = all[:nb_for_train]
+            values_for_test = all[nb_for_train:]
+
         self.train_input, self.train_stack_counts = stack.generate_sequences(
-            nb_train_samples, nb_steps, nb_stacks, nb_digits, self.device
+            nb_train_samples,
+            nb_steps,
+            nb_stacks,
+            nb_digits,
+            values_for_train,
+            self.device,
         )
 
         self.test_input, self.test_stack_counts = stack.generate_sequences(
-            nb_test_samples, nb_steps, nb_stacks, nb_digits, self.device
+            nb_test_samples,
+            nb_steps,
+            nb_stacks,
+            nb_digits,
+            values_for_test,
+            self.device,
         )
 
         mask = self.test_input.clone()
@@ -946,7 +968,7 @@ class TaskStack(Task):
             )
 
             #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-            input = self.test_input[:10, :20]
+            input = self.test_input[:10, :50]
             result = input.clone()
             stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
             ar_mask = (result != input).long()
@@ -1038,6 +1060,7 @@ elif args.task == "stack":
         nb_steps=args.stack_nb_steps,
         nb_stacks=args.stack_nb_stacks,
         nb_digits=args.stack_nb_digits,
+        fraction_values_for_train=args.stack_fraction_values_for_train,
         device=device,
     )
 
diff --git a/stack.py b/stack.py
index 675182e..219a1ad 100755
--- a/stack.py
+++ b/stack.py
@@ -13,7 +13,9 @@ import torch, torchvision
 # CODE_VAL=val + 2 * nb_stacks
 
 
-def generate_sequences(nb, nb_steps, nb_stacks, nb_digits, device=torch.device("cpu")):
+def generate_sequences(
+    nb, nb_steps, nb_stacks, nb_digits, values=None, device=torch.device("cpu")
+):
     stack = torch.empty(nb, nb_stacks, nb_steps, dtype=torch.int64)
     stack_counts = torch.zeros(nb, nb_stacks, dtype=torch.int64)
     k = torch.arange(nb)
@@ -26,7 +28,10 @@ def generate_sequences(nb, nb_steps, nb_stacks, nb_digits, device=torch.device("
         op = torch.randint(2, (nb,))
         st = torch.randint(nb_stacks, (nb,))
         op = op * (stack_counts[k, st] > 0)
-        val_push = torch.randint(10**nb_digits, (nb,))
+        if values is None:
+            val_push = torch.randint(10**nb_digits, (nb,))
+        else:
+            val_push = values[torch.randint(values.size(0), (nb,))]
         val_pop = stack[
             k,
             st,