DL with fastai Learner
from fastai.vision.all import *
path = untar_data(URLs.MNIST_SAMPLE)
Path.BASE_PATH = path
path.ls()
def get_dls():
def get_ds(train_valid):
def get_X(train_valid, three_seven):
files = (path/train_valid/three_seven).ls()
X = torch.stack([tensor(Image.open(x)) for x in files])#.reshape(-1, 28*28)
return X
X3, X7 = get_X(train_valid, '3'), get_X(train_valid, '7')
X = torch.cat([X3, X7])/255.
y = torch.tensor([1]*len(X3) + [0]*len(X7)).reshape(-1, 1)
#print(X.shape, y.shape)
return list(zip(X, y))
ds1 = DataLoader(get_ds('train'), bs=256, shuffle=True)
ds2 = DataLoader(get_ds('valid'), bs=256)
return DataLoaders(ds1, ds2)
dls = get_dls()
def loss_fn(y, t):
return torch.where(t==1, 1-y, y).mean()
model = nn.Sequential(
nn.Flatten(),
nn.Linear(28*28,30),
nn.ReLU(),
nn.Linear(30,1),
nn.Sigmoid(),
)
def batch_accuracy(y, t):
return ((y>0.5)==t).float().mean()
learn = Learner(dls, model, opt_func=SGD, loss_func=loss_fn, metrics=batch_accuracy)
learn.fit(40, 0.1)
plt.plot(L(learn.recorder.values).itemgot(2));
