The validation error was computed on the training data. That's embarassing.

[dyncnn.git] / dyncnn.lua

diff --git a/dyncnn.lua b/dyncnn.lua
index 7cc5318..839431a 100755 (executable)
--- a/dyncnn.lua
+++ b/dyncnn.lua
@@ -642,7 +642,7 @@ function trainModel(model,
          local startTime = sys.clock()
 
          for b = 1, validationData.nbSamples, batchSize do
-            fillBatch(trainData, b, batchSize, batch)
+            fillBatch(validationData, b, batchSize, batch)
             local output = model:forward(batch.input)
             accLoss = accLoss + l2criterion:forward(output, batch.target)
             nbBatches = nbBatches + 1