batch_nb_mlps, 2 * nb_samples, dtype=torch.int64, device=device
)
+ nb_rec = 8
+ nb_values = 2 # more increases the min-max gap
+
+ rec_support = torch.empty(batch_nb_mlps, nb_rec, 4, device=device)
+
while (data_targets.float().mean(-1) - 0.5).abs().max() > 0.1:
i = (data_targets.float().mean(-1) - 0.5).abs() > 0.1
nb = i.sum()
-
- nb_rec = 8
- nb_values = 2 # more increases the min-max gap
support = torch.rand(nb, nb_rec, 2, nb_values, device=device) * 2 - 1
support = support.sort(-1).values
support = support[:, :, :, torch.tensor([0, nb_values - 1])].view(nb, nb_rec, 4)
.values
)
- data_input[i], data_targets[i] = x, y
+ data_input[i], data_targets[i], rec_support[i] = x, y, support
train_input, train_targets = (
data_input[:, :nb_samples],
q_train_input = quantize(train_input, -1, 1)
train_input = dequantize(q_train_input, -1, 1)
- train_targets = train_targets
q_test_input = quantize(test_input, -1, 1)
test_input = dequantize(q_test_input, -1, 1)
- test_targets = test_targets
if save_as_examples:
- for k in range(q_train_input.size(0)):
- with open(f"example_{k:04d}.dat", "w") as f:
+ a = (
+ 2
+ * torch.arange(nb_quantization_levels).float()
+ / (nb_quantization_levels - 1)
+ - 1
+ )
+ xf = torch.cat(
+ [
+ a[:, None, None].expand(
+ nb_quantization_levels, nb_quantization_levels, 1
+ ),
+ a[None, :, None].expand(
+ nb_quantization_levels, nb_quantization_levels, 1
+ ),
+ ],
+ 2,
+ )
+ xf = xf.reshape(1, -1, 2).expand(min(q_train_input.size(0), 10), -1, -1)
+ print(f"{xf.size()=} {x.size()=}")
+ yf = (
+ (
+ (xf[:, None, :, 0] >= rec_support[: xf.size(0), :, None, 0]).long()
+ * (xf[:, None, :, 0] <= rec_support[: xf.size(0), :, None, 1]).long()
+ * (xf[:, None, :, 1] >= rec_support[: xf.size(0), :, None, 2]).long()
+ * (xf[:, None, :, 1] <= rec_support[: xf.size(0), :, None, 3]).long()
+ )
+ .max(dim=1)
+ .values
+ )
+
+ full_input, full_targets = xf, yf
+
+ q_full_input = quantize(full_input, -1, 1)
+ full_input = dequantize(q_full_input, -1, 1)
+
+ for k in range(q_full_input[:10].size(0)):
+ with open(f"example_full_{k:04d}.dat", "w") as f:
+ for u, c in zip(full_input[k], full_targets[k]):
+ f.write(f"{c} {u[0].item()} {u[1].item()}\n")
+
+ for k in range(q_train_input[:10].size(0)):
+ with open(f"example_train_{k:04d}.dat", "w") as f:
for u, c in zip(train_input[k], train_targets[k]):
f.write(f"{c} {u[0].item()} {u[1].item()}\n")
def evaluate_q_params(
- q_params, q_set, batch_size=25, device=torch.device("cpu"), nb_mlps_per_batch=1024,
- save_as_examples=False,
+ q_params,
+ q_set,
+ batch_size=25,
+ device=torch.device("cpu"),
+ nb_mlps_per_batch=1024,
+ save_as_examples=False,
):
errors = []
nb_mlps = q_params.size(0)
if __name__ == "__main__":
import time
- batch_nb_mlps, nb_samples = 128, 2500
+ batch_nb_mlps, nb_samples = 128, 250
generate_sets_and_params(
batch_nb_mlps=10,