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MNIST CNN





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%reload_ext autoreload
%autoreload 2
%matplotlib inline



    











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from fastai.vision import *

Data





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path = untar_data(URLs.MNIST)



    











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path.ls()



    


















    
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[PosixPath('/home/ubuntu/.fastai/data/mnist_png/training'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_png/testing')]

















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il = ImageList.from_folder(path, convert_mode='L')



    











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il.items[0]



    


















    
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PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/4/44688.png')

















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defaults.cmap='binary'



    











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il



    


















    
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ImageList (70000 items)
[Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28)]...
Path: /home/ubuntu/.fastai/data/mnist_png

















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il[0].show()



    


















    
























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sd = il.split_by_folder(train='training', valid='testing')



    











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sd



    


















    
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ItemLists;

Train: ImageList (60000 items)
[Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28)]...
Path: /home/ubuntu/.fastai/data/mnist_png;

Valid: ImageList (10000 items)
[Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28)]...
Path: /home/ubuntu/.fastai/data/mnist_png;

Test: None

















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(path/'training').ls()



    


















    
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[PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/4'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/6'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/8'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/0'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/9'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/1'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/3'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/2'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/5'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_png/training/7')]

















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ll = sd.label_from_folder()



    











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ll



    


















    
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LabelLists;

Train: LabelList
y: CategoryList (60000 items)
[Category 4, Category 4, Category 4, Category 4, Category 4]...
Path: /home/ubuntu/.fastai/data/mnist_png
x: ImageList (60000 items)
[Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28)]...
Path: /home/ubuntu/.fastai/data/mnist_png;

Valid: LabelList
y: CategoryList (10000 items)
[Category 4, Category 4, Category 4, Category 4, Category 4]...
Path: /home/ubuntu/.fastai/data/mnist_png
x: ImageList (10000 items)
[Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28), Image (1, 28, 28)]...
Path: /home/ubuntu/.fastai/data/mnist_png;

Test: None

















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x,y = ll.train[0]



    











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x.show()
print(y,x.shape)



    


















    






4 torch.Size([1, 28, 28])










    
























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tfms = ([*rand_pad(padding=3, size=28, mode='zeros')], [])



    











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ll = ll.transform(tfms)



    











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bs = 128



    











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# not using imagenet_stats because not using pretrained model
data = ll.databunch(bs=bs).normalize()



    











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x,y = data.train_ds[0]



    











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x.show()
print(y)



    


















    






4










    
























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def _plot(i,j,ax): data.train_ds[0][0].show(ax, cmap='gray')
plot_multi(_plot, 3, 3, figsize=(8,8))



    


















    
























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xb,yb = data.one_batch()
xb.shape,yb.shape



    


















    
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(torch.Size([128, 1, 28, 28]), torch.Size([128]))

















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data.show_batch(rows=3, figsize=(5,5))

Basic CNN with batchnorm





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def conv(ni,nf): return nn.Conv2d(ni, nf, kernel_size=3, stride=2, padding=1)



    











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model = nn.Sequential(
    conv(1, 8), # 14
    nn.BatchNorm2d(8),
    nn.ReLU(),
    conv(8, 16), # 7
    nn.BatchNorm2d(16),
    nn.ReLU(),
    conv(16, 32), # 4
    nn.BatchNorm2d(32),
    nn.ReLU(),
    conv(32, 16), # 2
    nn.BatchNorm2d(16),
    nn.ReLU(),
    conv(16, 10), # 1
    nn.BatchNorm2d(10),
    Flatten()     # remove (1,1) grid
)



    











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learn = Learner(data, model, loss_func = nn.CrossEntropyLoss(), metrics=accuracy)



    











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print(learn.summary())



    


















    






======================================================================
Layer (type)         Output Shape         Param #    Trainable 
======================================================================
Conv2d               [128, 8, 14, 14]     80         True      
______________________________________________________________________
BatchNorm2d          [128, 8, 14, 14]     16         True      
______________________________________________________________________
ReLU                 [128, 8, 14, 14]     0          False     
______________________________________________________________________
Conv2d               [128, 16, 7, 7]      1168       True      
______________________________________________________________________
BatchNorm2d          [128, 16, 7, 7]      32         True      
______________________________________________________________________
ReLU                 [128, 16, 7, 7]      0          False     
______________________________________________________________________
Conv2d               [128, 32, 4, 4]      4640       True      
______________________________________________________________________
BatchNorm2d          [128, 32, 4, 4]      64         True      
______________________________________________________________________
ReLU                 [128, 32, 4, 4]      0          False     
______________________________________________________________________
Conv2d               [128, 16, 2, 2]      4624       True      
______________________________________________________________________
BatchNorm2d          [128, 16, 2, 2]      32         True      
______________________________________________________________________
ReLU                 [128, 16, 2, 2]      0          False     
______________________________________________________________________
Conv2d               [128, 10, 1, 1]      1450       True      
______________________________________________________________________
BatchNorm2d          [128, 10, 1, 1]      20         True      
______________________________________________________________________
Flatten              [128, 10]            0          False     
______________________________________________________________________

Total params: 12126
Total trainable params: 12126
Total non-trainable params: 0


















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xb = xb.cuda()



    











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model(xb).shape



    


















    
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torch.Size([128, 10])

















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learn.lr_find(end_lr=100)



    


















    
















    






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

















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learn.recorder.plot()



    


















    
























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learn.fit_one_cycle(3, max_lr=0.1)



    


















    






Total time: 00:30 

  

    epoch
    train_loss
    valid_loss
    accuracy
  

  

    1
    0.223167
    0.217859
    0.930500
  

  

    2
    0.136179
    0.078651
    0.976400
  

  

    3
    0.072080
    0.038664
    0.988600

Refactor





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def conv2(ni,nf): return conv_layer(ni,nf,stride=2)



    











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model = nn.Sequential(
    conv2(1, 8),   # 14
    conv2(8, 16),  # 7
    conv2(16, 32), # 4
    conv2(32, 16), # 2
    conv2(16, 10), # 1
    Flatten()      # remove (1,1) grid
)



    











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learn = Learner(data, model, loss_func = nn.CrossEntropyLoss(), metrics=accuracy)



    











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learn.fit_one_cycle(10, max_lr=0.1)



    


















    






Total time: 01:12 

  

    epoch
    train_loss
    valid_loss
    accuracy
  

  

    1
    0.228332
    0.206325
    0.937500
  

  

    2
    0.189966
    0.192558
    0.940800
  

  

    3
    0.156765
    0.092810
    0.969100
  

  

    4
    0.135871
    0.083914
    0.973300
  

  

    5
    0.108844
    0.071582
    0.978000
  

  

    6
    0.105887
    0.128586
    0.960200
  

  

    7
    0.080699
    0.052754
    0.983200
  

  

    8
    0.066007
    0.037588
    0.988600
  

  

    9
    0.047513
    0.030255
    0.990200
  

  

    10
    0.044705
    0.028373
    0.991600

Resnet-ish





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class ResBlock(nn.Module):
    def __init__(self, nf):
        super().__init__()
        self.conv1 = conv_layer(nf,nf)
        self.conv2 = conv_layer(nf,nf)
        
    def forward(self, x): return x + self.conv2(self.conv1(x))



    











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help(res_block)



    


















    






Help on function res_block in module fastai.layers:

res_block(nf, dense:bool=False, norm_type:Union[fastai.layers.NormType, NoneType]=<NormType.Batch: 1>, bottle:bool=False, **kwargs)
    Resnet block of `nf` features.


















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model = nn.Sequential(
    conv2(1, 8),
    res_block(8),
    conv2(8, 16),
    res_block(16),
    conv2(16, 32),
    res_block(32),
    conv2(32, 16),
    res_block(16),
    conv2(16, 10),
    Flatten()
)



    











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def conv_and_res(ni,nf): return nn.Sequential(conv2(ni, nf), res_block(nf))



    











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model = nn.Sequential(
    conv_and_res(1, 8),
    conv_and_res(8, 16),
    conv_and_res(16, 32),
    conv_and_res(32, 16),
    conv2(16, 10),
    Flatten()
)



    











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learn = Learner(data, model, loss_func = nn.CrossEntropyLoss(), metrics=accuracy)



    











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learn.lr_find(end_lr=100)
learn.recorder.plot()



    


















    






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










    
























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learn.fit_one_cycle(12, max_lr=0.05)



    


















    






Total time: 02:00 

  

    epoch
    train_loss
    valid_loss
    accuracy
  

  

    1
    0.248807
    0.121582
    0.972800
  

  

    2
    0.120927
    0.360583
    0.890300
  

  

    3
    0.104013
    0.074916
    0.977800
  

  

    4
    0.081181
    0.065717
    0.980000
  

  

    5
    0.068514
    0.096448
    0.967200
  

  

    6
    0.061274
    0.087955
    0.971800
  

  

    7
    0.051673
    0.033911
    0.989400
  

  

    8
    0.048090
    0.033234
    0.988800
  

  

    9
    0.039095
    0.024638
    0.992400
  

  

    10
    0.023670
    0.021215
    0.993400
  

  

    11
    0.019128
    0.016159
    0.994500
  

  

    12
    0.021365
    0.016120
    0.995200
  




















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print(learn.summary())



    


















    






======================================================================
Layer (type)         Output Shape         Param #    Trainable 
======================================================================
Conv2d               [128, 8, 14, 14]     72         True      
______________________________________________________________________
ReLU                 [128, 8, 14, 14]     0          False     
______________________________________________________________________
BatchNorm2d          [128, 8, 14, 14]     16         True      
______________________________________________________________________
Conv2d               [128, 8, 14, 14]     576        True      
______________________________________________________________________
ReLU                 [128, 8, 14, 14]     0          False     
______________________________________________________________________
BatchNorm2d          [128, 8, 14, 14]     16         True      
______________________________________________________________________
Conv2d               [128, 8, 14, 14]     576        True      
______________________________________________________________________
ReLU                 [128, 8, 14, 14]     0          False     
______________________________________________________________________
BatchNorm2d          [128, 8, 14, 14]     16         True      
______________________________________________________________________
MergeLayer           [128, 8, 14, 14]     0          False     
______________________________________________________________________
Conv2d               [128, 16, 7, 7]      1152       True      
______________________________________________________________________
ReLU                 [128, 16, 7, 7]      0          False     
______________________________________________________________________
BatchNorm2d          [128, 16, 7, 7]      32         True      
______________________________________________________________________
Conv2d               [128, 16, 7, 7]      2304       True      
______________________________________________________________________
ReLU                 [128, 16, 7, 7]      0          False     
______________________________________________________________________
BatchNorm2d          [128, 16, 7, 7]      32         True      
______________________________________________________________________
Conv2d               [128, 16, 7, 7]      2304       True      
______________________________________________________________________
ReLU                 [128, 16, 7, 7]      0          False     
______________________________________________________________________
BatchNorm2d          [128, 16, 7, 7]      32         True      
______________________________________________________________________
MergeLayer           [128, 16, 7, 7]      0          False     
______________________________________________________________________
Conv2d               [128, 32, 4, 4]      4608       True      
______________________________________________________________________
ReLU                 [128, 32, 4, 4]      0          False     
______________________________________________________________________
BatchNorm2d          [128, 32, 4, 4]      64         True      
______________________________________________________________________
Conv2d               [128, 32, 4, 4]      9216       True      
______________________________________________________________________
ReLU                 [128, 32, 4, 4]      0          False     
______________________________________________________________________
BatchNorm2d          [128, 32, 4, 4]      64         True      
______________________________________________________________________
Conv2d               [128, 32, 4, 4]      9216       True      
______________________________________________________________________
ReLU                 [128, 32, 4, 4]      0          False     
______________________________________________________________________
BatchNorm2d          [128, 32, 4, 4]      64         True      
______________________________________________________________________
MergeLayer           [128, 32, 4, 4]      0          False     
______________________________________________________________________
Conv2d               [128, 16, 2, 2]      4608       True      
______________________________________________________________________
ReLU                 [128, 16, 2, 2]      0          False     
______________________________________________________________________
BatchNorm2d          [128, 16, 2, 2]      32         True      
______________________________________________________________________
Conv2d               [128, 16, 2, 2]      2304       True      
______________________________________________________________________
ReLU                 [128, 16, 2, 2]      0          False     
______________________________________________________________________
BatchNorm2d          [128, 16, 2, 2]      32         True      
______________________________________________________________________
Conv2d               [128, 16, 2, 2]      2304       True      
______________________________________________________________________
ReLU                 [128, 16, 2, 2]      0          False     
______________________________________________________________________
BatchNorm2d          [128, 16, 2, 2]      32         True      
______________________________________________________________________
MergeLayer           [128, 16, 2, 2]      0          False     
______________________________________________________________________
Conv2d               [128, 10, 1, 1]      1440       True      
______________________________________________________________________
ReLU                 [128, 10, 1, 1]      0          False     
______________________________________________________________________
BatchNorm2d          [128, 10, 1, 1]      20         True      
______________________________________________________________________
Flatten              [128, 10]            0          False     
______________________________________________________________________

Total params: 41132
Total trainable params: 41132
Total non-trainable params: 0

fin





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Content source: fastai/course-v3

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