Fix loss explosion?

src/models/residual3.py

@@ -2,6 +2,9 @@ import torch
 import torch.nn as nn
 
 
+EPSILON = 1e-5
+
+
 class Residual3(nn.Module):
     def __init__(
         self,
@@ -124,11 +127,11 @@ class Residual3(nn.Module):
         out = self.deconv2(out)
         out = self.deconv3(out)
 
-        return self._normalize(out)
+        return out
 
-    def _normalize(self, x):
+    # def _normalize(self, x):
-        x_sum = torch.sum(x, dim=(2, 3, 4), keepdim=True)
+    #     x_sum = torch.sum(x, dim=(2, 3, 4), keepdim=True)
-        return x / (x_sum + 1e-6)
+    #     return x / (x_sum + EPSILON)
 
     def _initialize_weights(self):
         for m in self.modules():

src/training/train.py

@@ -4,19 +4,21 @@ from torch.utils.tensorboard import SummaryWriter
 from pathlib import Path
 from torch.optim import Adam
 from tqdm.notebook import tqdm
-from .get_next_run_name import get_next_run_name
 from visualisation import plot_histograms_in_2d
-from models import create_model, save_model
+from models import create_model
 from datetime import timedelta, datetime
 from torch.utils.data import DataLoader
 import torch
 from utils import serialise_hparams
 
 
+EPSILON = 1e-5
+
+
 def train(
     hyperparameters: Dict[str, Any],
-    training_data_path: DataLoader,
+    train_data_loader: DataLoader,
-    test_data_path: DataLoader,
+    test_data_loader: DataLoader,
     log_dir: Path,
     max_duration: Optional[timedelta],
     use_tqdm: bool,
@@ -31,9 +33,6 @@ def train(
     start_time = datetime.now()
 
     with SummaryWriter(log_dir) as writer:
-        train_data_loader = training_data_path
-        test_data_loader = test_data_path
-
         model = create_model(
             type=model_type,
             bin_count=bin_count,
@@ -65,7 +64,7 @@ def train(
                 optimizer.zero_grad()
                 predicted_original = model(edited_histogram.to(device))
                 loss = loss_function(
-                    torch.log(torch.clamp(predicted_original, 1e-5, 1)),
+                    torch.log(torch.clamp(predicted_original, EPSILON, 1)),
                     original_histogram.to(device),
                 )
 
@@ -102,7 +101,7 @@ def train(
                 ):
                     predicted_original = model(edited_histogram.to(device))
                     epoch_test_loss += loss_function(
-                        torch.log(torch.clamp(predicted_original, 1e-10, 1)),
+                        torch.log(torch.clamp(predicted_original, EPSILON, 1)),
                         original_histogram.to(device),
                     ).item()
             writer.add_hparams(