This notebook demonstrates various techniques of effective Neural Network models training using the Callbacks mechanism of FastAI library (v1).

Getting the Data

from fastai.vision import *

from fastai.callbacks import *

path = untar_data(URLs.CIFAR) 

Downloading http://files.fast.ai/data/examples/cifar10

data = (ImageList.from_folder(path)
        .split_by_rand_pct(0.2)
        .label_from_folder()
        .transform(get_transforms())
        .add_test_folder()
        .databunch(bs=256)
        .normalize(imagenet_stats))

data

ImageDataBunch;

Train: LabelList (48000 items)
x: ImageList
Image (3, 32, 32),Image (3, 32, 32),Image (3, 32, 32),Image (3, 32, 32),Image (3, 32, 32)
y: CategoryList
ship,ship,ship,ship,ship
Path: /root/.fastai/data/cifar10;

Valid: LabelList (12000 items)
x: ImageList
Image (3, 32, 32),Image (3, 32, 32),Image (3, 32, 32),Image (3, 32, 32),Image (3, 32, 32)
y: CategoryList
ship,airplane,ship,truck,cat
Path: /root/.fastai/data/cifar10;

Test: LabelList (10000 items)
x: ImageList
Image (3, 32, 32),Image (3, 32, 32),Image (3, 32, 32),Image (3, 32, 32),Image (3, 32, 32)
y: EmptyLabelList
,,,,
Path: /root/.fastai/data/cifar10
data.classes

['airplane',
 'automobile',
 'bird',
 'cat',
 'deer',
 'dog',
 'frog',
 'horse',
 'ship',
 'truck']
data.show_batch(figsize=(6,6))

results = pd.DataFrame(index=range(5))

Randomly Initialized CONV Model

int(torch.cuda.max_memory_allocated(0)/(1024*1024))

4
learn = cnn_learner(data, models.resnet50, metrics=[accuracy], pretrained=False, callback_fns=[CSVLogger,ShowGraph])

learn.lr_find()
learn.recorder.plot()

LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.

learn.fit_one_cycle(5,max_lr=2e-2)
epoch train_loss valid_loss accuracy time
0 2.357704 2.542866 0.225500 01:49
1 1.929239 102.319199 0.286333 01:47
2 1.584136 56.509899 0.443833 01:47
3 1.341141 151.094666 0.551667 01:47
4 1.221145 56.869053 0.580333 01:46 
results['resnet50'] = pd.read_csv(path/'history.csv',usecols=['accuracy'])
results.head()
resnet50
0 0.225500
1 0.286333
2 0.443833
3 0.551667
4 0.580333 
results.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7efd4f723be0>

Pretrained CONV net Model

learn = cnn_learner(data, models.resnet101, metrics=[accuracy], pretrained=True, callback_fns=[CSVLogger,ShowGraph])

learn.lr_find()
learn.recorder.plot()

LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.

learn.fit_one_cycle(5,max_lr=2e-1)
epoch train_loss valid_loss accuracy time
0 2.271758 2.654338 0.235583 01:44
1 2.068404 2.629152 0.278167 01:42
2 1.575158 1.778586 0.429750 01:41
3 1.277541 1.413217 0.536667 01:41
4 1.066930 12.048190 0.635917 01:40 
results['resnet101 stage-1'] = pd.read_csv(path/'history.csv',usecols=['accuracy'])
ax = results.plot(title='Model\'s Accuracy',xticks=[0,1,2,3,4])
ax.set_xlabel("Epoch of the Stage")

<matplotlib.axes._subplots.AxesSubplot at 0x7f4c9055db00>
learn.save('stage-1')

learn.load('stage-1');

learn.unfreeze()

learn.lr_find()
learn.recorder.plot()

LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.

learn.fit_one_cycle(5,max_lr=slice(1e-6,1e-5))
epoch train_loss valid_loss accuracy time
0 1.026937 6.422010 0.642917 01:49
1 1.018373 1.010670 0.642333 01:49
2 0.990238 5.418860 0.646667 01:49
3 0.995587 1.666115 0.645833 01:49
4 0.979671 1.628434 0.651333 01:50 
learn.save('stage-2')

results['resnet101 stage-2'] = pd.read_csv(path/'history.csv',usecols=['accuracy'])
ax = results.plot(title='Model\'s Accuracy',xticks=[0,1,2,3,4])
ax.set_xlabel("Epoch of the Stage")

<matplotlib.axes._subplots.AxesSubplot at 0x7f4c98611080>
learn.lr_find(start_lr=1e-9)
learn.recorder.plot()

LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.

learn.fit_one_cycle(5,max_lr=slice(1e-6,1e-5))
epoch train_loss valid_loss accuracy time
0 0.974124 7.144644 0.659167 01:50
1 0.984295 8.854224 0.656667 01:49
2 0.969902 7.799942 0.653583 01:50
3 0.958520 1.253303 0.659667 01:50
4 0.947820 1.158069 0.664083 01:50 
results['resnet101 stage-3'] = pd.read_csv(path/'history.csv',usecols=['accuracy'])
ax = results.plot(title='Model\'s Accuracy',xticks=[0,1,2,3,4])
ax.set_xlabel("Epoch of the Stage")

Text(0.5, 0, 'Epoch of the Stage')
learn.save('stage-3')

learn.lr_find()
learn.recorder.plot()

LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.

learn.fit_one_cycle(5,max_lr=slice(3e-6,3e-5))
epoch train_loss valid_loss accuracy time
0 0.485009 0.488910 0.831667 01:19
1 0.473261 0.488016 0.831667 01:18
2 0.458254 0.475492 0.833500 01:16
3 0.450998 0.471915 0.839000 01:15
4 0.440244 0.474717 0.836833 01:15 
learn.save('stage-4')

results['resnet50 Stage-4'] = pd.read_csv(path/'history.csv',usecols=['accuracy'])
results.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fc4570c6908>

Results

interp = ClassificationInterpretation.from_learner(learn)
losses,idxs = interp.top_losses()

interp.plot_confusion_matrix(figsize=(8,8))

interp.plot_top_losses(25, figsize=(13,13))

interp.most_confused(min_val=3)

[('dog', 'cat', 106),
 ('cat', 'dog', 58),
 ('cat', 'frog', 50),
 ('bird', 'frog', 38),
 ('dog', 'horse', 34),
 ('bird', 'deer', 31),
 ('automobile', 'truck', 30),
 ('truck', 'automobile', 30),
 ('ship', 'airplane', 28),
 ('deer', 'bird', 27),
 ('deer', 'frog', 25),
 ('cat', 'deer', 24),
 ('bird', 'cat', 23),
 ('dog', 'deer', 23),
 ('bird', 'dog', 22),
 ('airplane', 'ship', 21),
 ('cat', 'bird', 19),
 ('bird', 'airplane', 18),
 ('horse', 'cat', 18),
 ('deer', 'cat', 17),
 ('dog', 'frog', 17),
 ('deer', 'horse', 16),
 ('dog', 'bird', 16),
 ('horse', 'deer', 15),
 ('cat', 'horse', 14),
 ('airplane', 'bird', 13),
 ('frog', 'cat', 13),
 ('ship', 'truck', 13),
 ('airplane', 'truck', 12),
 ('frog', 'bird', 12),
 ('ship', 'automobile', 12),
 ('bird', 'horse', 11),
 ('airplane', 'automobile', 10),
 ('horse', 'dog', 10),
 ('cat', 'ship', 9),
 ('deer', 'dog', 9),
 ('airplane', 'deer', 8),
 ('automobile', 'ship', 7),
 ('cat', 'airplane', 7),
 ('truck', 'ship', 7),
 ('airplane', 'cat', 6),
 ('frog', 'deer', 5),
 ('horse', 'bird', 5),
 ('ship', 'cat', 5),
 ('truck', 'horse', 5),
 ('automobile', 'airplane', 4),
 ('bird', 'automobile', 4),
 ('bird', 'ship', 4),
 ('cat', 'truck', 4),
 ('deer', 'airplane', 4),
 ('dog', 'ship', 4),
 ('truck', 'airplane', 4),
 ('truck', 'cat', 4),
 ('truck', 'frog', 4),
 ('airplane', 'horse', 3),
 ('cat', 'automobile', 3),
 ('frog', 'dog', 3),
 ('horse', 'frog', 3)]