The fastai library simplifies training fast and accurate neural nets using modern best practices. See the fastai website to get started. The library is based on research into deep learning best practices undertaken at `fast.ai`

, and includes “out of the box” support for `vision`

, `text`

, `tabular`

, and `collab`

(collaborative filtering) models.

Download and prepare data:

`URLs_MNIST_SAMPLE()`

Transformations:

```
# transformations
= aug_transforms(do_flip = FALSE)
tfms = 'mnist_sample'
path = 20
bs
#load into memory
= ImageDataLoaders_from_folder(path, batch_tfms = tfms, size = 26, bs = bs)
data
= cnn_learner(data, resnet18(), metrics = accuracy) learn
```

`Cbs`

argument means callbacks:

`%>% fit_one_cycle(1, cbs = TerminateOnNaNCallback()) learn `

`%>% fit_one_cycle(10, cbs = EarlyStoppingCallback(monitor='valid_loss', patience = 1)) learn `

```
epoch train_loss valid_loss accuracy time
0 0.023524 0.009781 0.996565 00:16
1 0.033328 0.019839 0.993621 00:16
No improvement since epoch 0: early stopping
```

Save best model for each epoch:

```
= cnn_learner(data, resnet18(), metrics = accuracy, path = getwd())
learn
%>% fit_one_cycle(3, cbs = SaveModelCallback(every_epoch = TRUE, fname = 'model')) learn
```

See folder:

```
list.files('models')
# [1] "model_0.pth" "model_1.pth" "model_2.pth"
```

`# [1] "model_0.pth" "model_1.pth" "model_2.pth"`

Decrease learning rate if loss is not improved:

`%>% fit_one_cycle(10, 1e-2, cbs = ReduceLROnPlateau(monitor='valid_loss', patience = 1)) learn `

```
epoch train_loss valid_loss accuracy time
0 0.117138 0.038180 0.987242 00:17
1 0.140064 0.006160 0.996565 00:16
2 0.133680 0.061945 0.985770 00:16
Epoch 2: reducing lr to 0.0009891441414237997
3 0.049780 0.005699 0.998037 00:16
4 0.040660 0.019514 0.994112 00:16
Epoch 4: reducing lr to 0.0007502954607977343
5 0.027146 0.009783 0.997056 00:16
Epoch 5: reducing lr to 0.0005526052040192481
6 0.024709 0.008050 0.998528 00:16
Epoch 6: reducing lr to 0.0003458198506447947
7 0.016352 0.010778 0.998037 00:16
Epoch 7: reducing lr to 0.0001656946233635187
8 0.071180 0.009519 0.998528 00:16
Epoch 8: reducing lr to 4.337456332530222e-05
9 0.014804 0.005769 0.998528 00:16
Epoch 9: reducing lr to 1.0114427793916913e-08
```

Or add new parameter `min_lr`

:

```
%>% fit_one_cycle(10, 1e-2, cbs = ReduceLROnPlateau(monitor='valid_loss',
learn min_delta=0.1, patience = 1, min_lr = 1e-8))
```

Save train history. In addition, for multiple callbacks it is important to pass them within list:

```
= cnn_learner(data, resnet18(), metrics = accuracy, path = getwd())
learn
%>% fit_one_cycle(2, cbs = list(CSVLogger(),
learn ReduceLROnPlateau(monitor='valid_loss',
min_delta=0.1, patience = 1, min_lr = 1e-8)))
= read.csv('history.csv')
history history
```

```
epoch train_loss valid_loss accuracy time
1 0 0.15677054 0.09788394 0.9646713 00:17
2 1 0.08268011 0.05654754 0.9803729 00:17
```