From 0522f82aa24682d0ea8133e9088756e3938a8b17 Mon Sep 17 00:00:00 2001
From: umlauf <>
Date: Fri, 24 Feb 2023 13:45:43 +0100
Subject: [PATCH] CEL small change
---
Code/train.py | 11 +++++++----
1 file changed, 7 insertions(+), 4 deletions(-)
diff --git a/Code/train.py b/Code/train.py
index e0ce706..1dd7ad4 100644
--- a/Code/train.py
+++ b/Code/train.py
@@ -144,11 +144,14 @@ def cross_entropy(logits, target):
Computes the cross-entropy loss between the predicted logits and the target labels.
Args:
- - logits (torch.Tensor): A tensor of shape (batch_size, num_classes) representing the predicted logits for each input example.
- - target (torch.Tensor): A tensor of shape (batch_size,) representing the target labels for each input example.
+ - logits (torch.Tensor): A tensor of shape (batch_size, num_classes)
+ representing the predicted logits for each input example.
+ - target (torch.Tensor): A tensor of shape (batch_size,)
+ representing the target labels for each input example.
Returns:
- - batch_loss (torch.Tensor): A scalar tensor representing the average cross-entropy loss across the batch.
+ - batch_loss (torch.Tensor): A scalar tensor representing
+ the average cross-entropy loss across the batch.
"""
results = torch.tensor([], device='cuda')
for i in range (logits.shape[0]):
@@ -168,7 +171,7 @@ def cross_entropy(logits, target):
loss_mixed_labels = -((mixed_vec[0] * logprobs[0][0]) + (mixed_vec[1] * logprobs[0][1]))
#calculation for mixed with indexing
results = torch.cat((loss_mixed_labels.view(1), results), dim=0)#append resultts to result tensor
- print("ALL BATCH Results: ", results)
+ print("LOSS BATCH (Results): ", results)
batch_loss = results.mean() #compute average of all results
return batch_loss
--
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