I’m working on it, @jeremy

I see some differences between the output of the two `log_preds = learn.predict()`

and `log_preds,y = learn.TTA()`

--> the sum of the probabilities computed from learn.predict is 1, while the sum of the probabilities computed from learn.TTA is less than 1.

```
log_preds,y = learn.TTA()
probs = np.exp(log_preds)
probs[:3,:]
array([[ 0.24539, 0.09634, 0.00952, 0.07535, 0.08283, 0.11779, 0.01073, 0.01777],
[ 0.39541, 0.03649, 0.02922, 0.21219, 0.01865, 0.11463, 0.02149, 0.07635],
[ 0.49963, 0.01744, 0.14847, 0.00922, 0.10761, 0.03257, 0.03157, 0.04786]], dtype=float32)
```

where the sum of `probs`

on each line is less than 1

and

```
log_preds = learn.predict()
probs = np.exp(log_preds)
probs[:3,:]
array([[ 0.19239, 0.10078, 0.01741, 0.02733, 0.34674, 0.26757, 0.01891, 0.02886],
[ 0.31218, 0.04001, 0.03897, 0.32251, 0.02798, 0.0951 , 0.04427, 0.11897],
[ 0.61758, 0.01188, 0.18024, 0.00552, 0.15407, 0.00619, 0.01183, 0.01269]], dtype=float32)
```

where the sum of `probs`

of each line is 1

In any case to display the most correct/incorrect images - we will use the `learn.predict()`

function.