From b5d9e725b3c8779d70dadf45b869f634b7a7d826 Mon Sep 17 00:00:00 2001 From: "Michael Pilosov, PhD" Date: Sat, 27 Jan 2024 19:45:50 +0000 Subject: [PATCH] allow for mix of supervised and not with alpha --- model.py | 6 +++--- newsearch.py | 7 +++++-- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/model.py b/model.py index 7302f69..7486af3 100644 --- a/model.py +++ b/model.py @@ -49,7 +49,7 @@ class ColorTransformerModel(L.LightningModule): inputs, outputs, ) - # alpha = self.hparams.alpha # TODO: decide what to do with this... + alpha = self.hparams.alpha # loss = p_loss # distance = torch.minimum( @@ -58,14 +58,14 @@ class ColorTransformerModel(L.LightningModule): distance = torch.norm(outputs - labels).mean() # Backprop with this: - loss = p_loss + loss = (1 - alpha) * p_loss + alpha * distance # p_loss is unsupervised # distance is supervised. self.log("hp_metric", loss) # Log all losses individually - self.log("train_mse", distance) self.log("train_pres", p_loss) + self.log("train_mse", distance) return loss def validation_step(self, batch): diff --git a/newsearch.py b/newsearch.py index 3fd09f9..4132a5d 100644 --- a/newsearch.py +++ b/newsearch.py @@ -24,10 +24,13 @@ NUM_JOBS = 100 # alpha_values = list(np.round(np.linspace(2, 4, 21), 4)) # learning_rate_values = list(np.round(np.logspace(-5, -3, 21), 5)) learning_rate_values = [1e-3] -alpha_values = [0] +# learning_rate_values = [5e-4] + +alpha_values = [0, .25, 0.5, 0.75, 1] # alpha = 0 is unsupervised. alpha = 1 is supervised. widths = [2**k for k in range(4, 13)] depths = [1, 2, 4, 8, 16] -# learning_rate_values = [5e-4] +# widths, depths = [128, 256], [4, 8] + batch_size_values = [256] max_epochs_values = [10] seeds = list(range(21, 1992))