DL without Pytorch nn.Module
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()
def batch_accuracy(y, t):
return ((y>0.5)==t).float().mean()
def validate_epoch(model):
accs = [batch_accuracy(model(xb.reshape(-1, 28*28)), yb) for xb,yb in dls.valid]
return round(torch.stack(accs).mean().item(), 4)
class BasicOptim:
def __init__(self,params, lr):
self.params = list(params)
self.lr = lr
def step(self, *args, **kwargs):
for p in self.params:
p.data -= p.grad.data * self.lr
def zero_grad(self, *args, **kwargs):
for p in self.params:
p.grad = None
W = torch.randn(28*28, 1).requires_grad_()
b = torch.randn(1).requires_grad_()
params = W, b
def model(X):
return sigmoid(X@W + b)
opt = BasicOptim(params, lr=0.1)
acc = []
for epoch in range(40):
for X, t in dls.train:
y = model(X.reshape(-1, 28*28))
loss = loss_fn(y, t)
loss.backward()
opt.step()
opt.zero_grad()
acc.append(validate_epoch(model))
import pandas as pd
pd.DataFrame({'accuracy':acc}).plot(grid=True)
