Hello,

I have a regression problem with a tabular data. Hence, the loss function which I am using is MSELossFlat and the metric is mean_squared_error. These should obviously give the same result on the validation set. However, it is not the case. The output is:

```
|epoch|train_loss|valid_loss|mean_squared_error|
|1| 0.047028| 0.008886| 2.268168|
|2| 0.041831| 0.034416| 2.090547|
|3| 0.030867| 0.007301| 2.322598|
|4| 0.038007| 0.012626| 2.236360|
|5| 0.033619| 0.010785| 2.593846|
```

So, valid_loss and the mean_squared_error metric on the validation set are very different. Would would be the reason for that? Can it be bug.

This experiment s easily reproducible by running the tabular.ipynb in the example folder and modifying 2 cells. The third cell I modify as:

```
from sklearn import preprocessing
#df['salary'].unique()
df['salary'] = preprocessing.StandardScaler().fit_transform( (df['age'] + df['fnlwgt']).values.reshape(-1,1) )
```

In the cell with the learner definition I pass the loss and the metric explicitely, also change the number pf epochs:

```
from fastai.layers import MSELossFlat
from fastai.metrics import mean_squared_error
learn = tabular_learner(data, layers=[200,100], loss_func=MSELossFlat(), metrics=mean_squared_error)
learn.fit(5, 1e-2)
```

Do you have any thoughts on this behaviour?

Thanks,

Alex.