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Update.
author
François Fleuret
<francois@fleuret.org>
Thu, 8 Feb 2024 06:24:13 +0000
(07:24 +0100)
committer
François Fleuret
<francois@fleuret.org>
Thu, 8 Feb 2024 06:24:13 +0000
(07:24 +0100)
main.py
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diff --git
a/main.py
b/main.py
index
4d5077a
..
91c885b
100755
(executable)
--- a/
main.py
+++ b/
main.py
@@
-87,7
+87,7
@@
parser.add_argument("--model", type=str, default=None)
parser.add_argument("--attention", type=str, default=None)
parser.add_argument("--attention", type=str, default=None)
-parser.add_argument("--pro
portion
_memex", type=float, default=0)
+parser.add_argument("--pro
ba
_memex", type=float, default=0)
parser.add_argument("--dim_model", type=int, default=None)
parser.add_argument("--dim_model", type=int, default=None)
@@
-738,7
+738,7
@@
log_string(f"device {device}")
vocabulary_size = task.vocabulary_size()
vocabulary_size = task.vocabulary_size()
-if args.pro
portion
_memex > 0:
+if args.pro
ba
_memex > 0:
vocabulary_size += 1
log_string(f"vocabulary_size {vocabulary_size}")
vocabulary_size += 1
log_string(f"vocabulary_size {vocabulary_size}")
@@
-902,22
+902,26
@@
for n_epoch in range(nb_epochs_finished, nb_epochs):
nb_train_samples, acc_train_loss, acc_train_inner_loss = 0, 0.0, 0.0
nb_train_samples, acc_train_loss, acc_train_inner_loss = 0, 0.0, 0.0
- def add_memex(batches, pro
portion
_memex):
+ def add_memex(batches, pro
ba
_memex):
for input in batches:
for input in batches:
- if torch.rand(1).item() < proportion_memex:
+ if torch.rand(1).item() < proba_memex:
+ sep = (
+ torch.full(
+ (input.size(0), 1), vocabulary_size - 1, device=input.device
+ ),
+ )
+
yield torch.cat(
[
input,
yield torch.cat(
[
input,
- torch.full(
- (input.size(0), 1), vocabulary_size - 1, device=input.device
- ),
+ sep,
input,
],
dim=1,
)
yield input
input,
],
dim=1,
)
yield input
- train_batches = add_memex(task.batches(split="train"), args.pro
portion
_memex)
+ train_batches = add_memex(task.batches(split="train"), args.pro
ba
_memex)
for input in train_batches:
model.reset_inner_loss()
for input in train_batches:
model.reset_inner_loss()