def _4e():
net = hw.DigitsConvNet()
train, val = utils.torch_digits()
n_epochs = 30
optimizer = optim.SGD(net.parameters(), lr=0.005)
loss_func = nn.CrossEntropyLoss()
train_el, val_el = hw.fit_and_validate(net, optimizer, loss_func, train, val, n_epochs, batch_size=16)
_4_plotter(train_el, val_el, 'SGD, lr = 0.005, batch = 16, epochs = 30')
def _4d():
net = hw.DigitsConvNet()
train, val = utils.torch_digits()
n_epochs = 30
optimizer = optim.SGD(net.parameters(), lr=0.005)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, 0.95)
loss_func = nn.CrossEntropyLoss()
train_el, val_el = hw.fit_and_validate(net, optimizer, loss_func, train, val, n_epochs, sch=scheduler)
torch.save(net.cpu().state_dict(), './conv.pb')
_4_plotter(train_el, val_el, 'SGD, lr = 0.005, gamma = 0.95, batch = 1, epochs = 30')
def _4f():
net = hw.DigitsConvNet()
train, val = utils.torch_digits()
n_epochs = 30
optimizer = optim.SGD(net.parameters(), lr=0.005)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, 0.95)
loss_func = nn.CrossEntropyLoss()
train_el, val_el = hw.fit_and_validate(net, optimizer, loss_func, train, val, n_epochs, batch_size=16, sch=scheduler)
digits, labels, val_X, val_Y = utils.torch_digits(mode=True)
intermediate = net.intermediate(digits)
utils.plot_PCA(intermediate.cpu().detach().numpy(), labels.cpu().detach().numpy())
def _4g():
net = hw.DigitsConvNet()
train, val = utils.torch_digits()
n_epochs = 30
optimizer = optim.SGD(net.parameters(), lr=0.005)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, 0.95)
loss_func = nn.CrossEntropyLoss()
train_el, val_el = hw.fit_and_validate(net, optimizer, loss_func, train, val, n_epochs, batch_size=16, sch=scheduler)
digits, labels, val_X, val_Y = utils.torch_digits(mode=True)
labels = labels.cpu().detach().numpy()
val_Y = val_Y.cpu().detach().numpy()
intermediate_train = net.intermediate(digits).cpu().detach().numpy()
intermediate_val = net.intermediate(val_X).cpu().detach().numpy()
knn = KNeighborsClassifier(n_neighbors=5, algorithm='kd_tree')
knn.fit(intermediate_train, labels)
accuracy = knn.score(intermediate_val, val_Y)
print(accuracy)