前者是培训损耗,后者是验证损耗
1。 在t raining 当中有用到 regularization,而 validation 中并没有 regularization。 2。trining loss 是在当前epoch 进行中计算出来的,而validation loss 是在当前epoch 训练完成 后计算出来的。这里有半个epoch 的时间差。在计算 validation loss的时候用的神经网络 其实比计算training loss 的时候是有进步的, ...
但是其variance还不够 考虑是否在训练集过拟合了 但是总体来说 validation略微回升也是比较常见的 ...
Finally, some training loss is greater than validation loss and some are lower. Here is how to have the same output from fit and evaluate : model.fit(x_train,y_train,validation_data=(x_train,y_train) model.evaluate(x_train,y_train) Then the metrics on the validation set from the fit...
So my question is, is having a validation loss lower than my training loss okay/acceptable? And how do I further reduce my validation loss? I'm aiming for something around 0.0x. Do I add more dropout layers or increase the dropout values? Reduce/increase number of neurons ...
incomplete representations. The training loss is higher because you've made it artificially harder for the network to give the right answers. However, during validation all of the units are available, so the network has its fullcomputational power- and thus it might perform better than in ...
Simply put, if training loss and validation loss are computed correctly, it is impossible for training loss to be higher than validation loss. This is because back-propagation DIRECTLY reduces error computed on the training set and only INDIRECTLY (not even guaranteed!) reduces error computed on ...
过拟合
Getting the validation loss during training seems to be a common issue: #1711 #1396 #310 The most common 'solution' is to set workflow = [('train', 1), ('val', 1)] . But when I do this, while adjusting the samples_per_gpu configuration, ...
I have used the Transformer model to train the time series dataset, but there is always a gap between training and validation in my loss curve. I have tried using different learning rates, batch sizes, dropout, heads, dim_feedforward, and layers, but they don't work. Can an...