Deep Learning for Coder: Chapter 5

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import fastbook
fastbook.setup_book()

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from fastai.vision.all import *
path = untar_data(URLs.PETS)

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path.ls()

(#2) [Path('/root/.fastai/data/oxford-iiit-pet/annotations'),Path('/root/.fastai/data/oxford-iiit-pet/images')]

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pets = DataBlock(
    blocks=(ImageBlock, CategoryBlock),
    get_items=get_image_files,
    splitter=RandomSplitter(seed=42),
    get_y=using_attr(RegexLabeller(r"(.+)_\d+.jpg$"), "name"),
    item_tfms=Resize(460),
    batch_tfms=aug_transforms(size=224, min_scale=0.75),
)
dls = pets.dataloaders(path/"images")

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dls.show_batch(nrows=3, ncols=3)

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learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(2)

Downloading: "https://download.pytorch.org/models/resnet34-b627a593.pth" to /root/.cache/torch/hub/checkpoints/resnet34-b627a593.pth

epoch	train_loss	valid_loss	error_rate	time
0	1.497884	0.302666	0.087280	01:02

epoch	train_loss	valid_loss	error_rate	time
0	0.494630	0.309310	0.099459	01:04
1	0.310687	0.254076	0.084574	01:06

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x, y = dls.one_batch()

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preds, _ = learn.get_preds(dl = [(x,y)])
torch.argmax(preds[0])

tensor(10)

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acts = torch.randn((6,2))*2
acts

tensor([[ 0.2126, -0.6733],
        [ 0.5868,  1.8977],
        [-1.5544,  0.4964],
        [-0.5226, -0.6044],
        [ 0.4885, -0.1626],
        [-3.0538, -2.0372]])

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acts.sigmoid().sum(1).unsqueeze(1)

tensor([[0.8907],
        [1.5123],
        [0.7961],
        [0.7256],
        [1.0792],
        [0.1604]])

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(acts[:,0]-acts[:,1]).sigmoid().unsqueeze(1)

tensor([[0.7080],
        [0.2123],
        [0.1140],
        [0.5204],
        [0.6573],
        [0.2657]])

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def softmax(x): return torch.exp(x) / torch.exp(x).sum(dim=1, keepdim=True)
sm_acts = softmax(acts)

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sm_acts

tensor([[0.7080, 0.2920],
        [0.2123, 0.7877],
        [0.1140, 0.8860],
        [0.5204, 0.4796],
        [0.6573, 0.3427],
        [0.2657, 0.7343]])

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targ = tensor([0,1,0,0,1,1])
sm_acts[range(6),targ].unsqueeze(1)

tensor([[0.7080],
        [0.7877],
        [0.1140],
        [0.5204],
        [0.3427],
        [0.7343]])

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-sm_acts[range(6),targ].log().mean()

tensor(0.7981)

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F.nll_loss(acts.log_softmax(dim=1), targ)

tensor(0.7981)

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loss_func = nn.CrossEntropyLoss(reduction='none')
loss_func(acts, targ)

tensor([0.3452, 0.2387, 2.1718, 0.6531, 1.0708, 0.3088])

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interp  = ClassificationInterpretation.from_learner(learn)
interp.plot_confusion_matrix(figsize=(12,12), dpi=60)

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interp.most_confused(min_val=5)

[('american_pit_bull_terrier', 'staffordshire_bull_terrier', np.int64(12)),
 ('Birman', 'Ragdoll', np.int64(7)),
 ('Egyptian_Mau', 'Bengal', np.int64(5)),
 ('american_pit_bull_terrier', 'american_bulldog', np.int64(5))]

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learn = vision_learner(dls, resnet34, metrics=error_rate)
learn.lr_find()

SuggestedLRs(valley=0.001737800776027143)

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learn = vision_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(2, base_lr=3e-3)

epoch	train_loss	valid_loss	error_rate	time
0	1.330929	0.305042	0.089986	01:07

epoch	train_loss	valid_loss	error_rate	time
0	0.523440	0.362486	0.104871	01:10
1	0.327184	0.269103	0.080514	01:06

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learn = vision_learner(dls, resnet34, metrics=error_rate)
learn.fit_one_cycle(3, 3e-2)

epoch	train_loss	valid_loss	error_rate	time
0	2.357152	3.695568	0.578484	00:12
1	1.188880	0.541915	0.169824	00:11
2	0.660204	0.358833	0.108931	00:11

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learn.unfreeze()

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learn.lr_find()

SuggestedLRs(valley=3.630780702224001e-05)

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learn.fit_one_cycle(6,  lr_max=1e-6)

epoch	train_loss	valid_loss	error_rate	time
0	0.474540	0.356742	0.108931	00:12
1	0.472879	0.353283	0.110284	00:12
2	0.471576	0.347057	0.103518	00:12
3	0.464339	0.345362	0.100812	00:12
4	0.468930	0.342762	0.106225	00:12
5	0.452638	0.343595	0.103518	00:12

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learn = vision_learner(dls, resnet34, metrics=error_rate)
learn.fit_one_cycle(3, 3e-3)
learn.unfreeze()
learn.fit_one_cycle(12, lr_max=slice(1e-6, 1e-4))

epoch	train_loss	valid_loss	error_rate	time
0	1.127102	0.334892	0.097429	00:11
1	0.518909	0.252394	0.085927	00:11
2	0.327980	0.218656	0.075778	00:11

epoch	train_loss	valid_loss	error_rate	time
0	0.243455	0.213250	0.074425	00:12
1	0.233122	0.212131	0.073748	00:12
2	0.220011	0.202811	0.063599	00:12
3	0.220371	0.196318	0.062923	00:12
4	0.191108	0.200352	0.064953	00:12
5	0.166688	0.201582	0.066982	00:12
6	0.155857	0.196664	0.062923	00:12
7	0.138741	0.190374	0.060217	00:12
8	0.118107	0.192674	0.067659	00:12
9	0.130965	0.191735	0.065629	00:12
10	0.124963	0.191818	0.066982	00:12
11	0.122479	0.191197	0.064276	00:12

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learn.recorder.plot_loss()

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from fastai.callback.fp16 import *
learn =  vision_learner(dls, resnet50, metrics=error_rate).to_fp16()
learn.fine_tune(6, freeze_epochs=3)

Downloading: "https://download.pytorch.org/models/resnet50-11ad3fa6.pth" to /root/.cache/torch/hub/checkpoints/resnet50-11ad3fa6.pth

epoch	train_loss	valid_loss	error_rate	time
0	2.191112	0.475555	0.154263	00:14
1	0.829924	0.294044	0.100812	00:13
2	0.550020	0.291130	0.096752	00:13

epoch	train_loss	valid_loss	error_rate	time
0	0.305628	0.236669	0.073748	00:14
1	0.242062	0.278092	0.081867	00:14
2	0.209351	0.231892	0.069689	00:14
3	0.133353	0.220406	0.064276	00:14
4	0.089182	0.202463	0.050744	00:14
5	0.065977	0.192152	0.052774	00:14