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Update
author
François Fleuret
<francois@fleuret.org>
Sat, 18 Mar 2023 13:08:20 +0000
(14:08 +0100)
committer
François Fleuret
<francois@fleuret.org>
Sat, 18 Mar 2023 13:08:20 +0000
(14:08 +0100)
beaver.py
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diff --git
a/beaver.py
b/beaver.py
index
c3b7e09
..
e22fc7b
100755
(executable)
--- a/
beaver.py
+++ b/
beaver.py
@@
-172,6
+172,7
@@
def compute_perplexity(model, split="train"):
def one_shot(gpt, task):
t = gpt.training
gpt.eval()
def one_shot(gpt, task):
t = gpt.training
gpt.eval()
+
model = nn.Sequential(
nn.Linear(args.dim_model, args.dim_model),
nn.ReLU(),
model = nn.Sequential(
nn.Linear(args.dim_model, args.dim_model),
nn.ReLU(),
@@
-190,11
+191,12
@@
def one_shot(gpt, task):
output = model(output_gpt)
targets = targets * (input.unsqueeze(-1) == maze.v_empty)
output = output * (input.unsqueeze(-1) == maze.v_empty)
output = model(output_gpt)
targets = targets * (input.unsqueeze(-1) == maze.v_empty)
output = output * (input.unsqueeze(-1) == maze.v_empty)
- loss = (
- -(output.log_softmax(-1) * targets).sum()
- / (input == maze.v_empty).sum()
- + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
- )
+ loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
+ # loss = (
+ # -(output.log_softmax(-1) * targets).sum()
+ # / (input == maze.v_empty).sum()
+ # + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
+ # )
acc_train_loss += loss.item() * input.size(0)
nb_train_samples += input.size(0)
acc_train_loss += loss.item() * input.size(0)
nb_train_samples += input.size(0)
@@
-208,11
+210,12
@@
def one_shot(gpt, task):
output = model(output_gpt)
targets = targets * (input.unsqueeze(-1) == maze.v_empty)
output = output * (input.unsqueeze(-1) == maze.v_empty)
output = model(output_gpt)
targets = targets * (input.unsqueeze(-1) == maze.v_empty)
output = output * (input.unsqueeze(-1) == maze.v_empty)
- loss = (
- -(output.log_softmax(-1) * targets).sum()
- / (input == maze.v_empty).sum()
- + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
- )
+ loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
+ # loss = (
+ # -(output.log_softmax(-1) * targets).sum()
+ # / (input == maze.v_empty).sum()
+ # + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
+ # )
acc_test_loss += loss.item() * input.size(0)
nb_test_samples += input.size(0)
acc_test_loss += loss.item() * input.size(0)
nb_test_samples += input.size(0)
@@
-225,9
+228,11
@@
def one_shot(gpt, task):
targets = task.test_policies[:32]
output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
output = model(output_gpt)
targets = task.test_policies[:32]
output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
output = model(output_gpt)
- losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
- losses = losses * (input == maze.v_empty)
- losses = losses / losses.max()
+ # losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
+ # losses = losses * (input == maze.v_empty)
+ # losses = losses / losses.max()
+ losses = (output.softmax(-1) - targets).abs().max(-1).values
+ losses = (losses >= 0.05).float()
losses = losses.reshape(-1, args.maze_height, args.maze_width)
input = input.reshape(-1, args.maze_height, args.maze_width)
maze.save_image(
losses = losses.reshape(-1, args.maze_height, args.maze_width)
input = input.reshape(-1, args.maze_height, args.maze_width)
maze.save_image(