def evaluate_dataset(net, dataset, batch_size=32):
"""
Args:
net: model
x: (num_samples, num_features)
y: (num_samples,) for binary label, or
(num_samples, num_classes) for multiclass label
"""
net.eval()
from torch.utils.data import DataLoader
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
n_correct = 0
loss_avg = 0
i = 0
for batch in data_loader:
i += 1
inputs, labels = batch
inputs = _H.tensor(inputs.numpy())
labels = _H.tensor(labels.numpy())
net.zero_grad()
output = net(inputs)
loss = _H.CrossEntropyLoss(output, labels).item()
loss_avg = (loss_avg * (i - 1) + loss) / i
pred = _np.argmax(output.data, axis=1)
n_correct += (pred == labels.data).sum()
net.train()
return n_correct / len(dataset), loss_avg
net = LeNetPlus()
optimizer = SGD(net.parameters(), lr=0.0003, momentum=0.9)
batch_size = 64
data_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
epochs = 100
for epoch in range(epochs):
print(epoch)
for i, batch in enumerate(data_loader):
inputs, labels = batch
inputs = H.tensor(inputs.numpy())
labels = H.tensor(labels.numpy())
net.zero_grad()
output = net(inputs)
loss = H.CrossEntropyLoss(output, labels)
loss.backward()
optimizer.step()
optimizer.reduce(0.95)
print(evaluate_dataset(net, val_data2, 64))
print(evaluate_dataset(net, val_data, 64))
# import networkx as nx
# def draw(g, **kwargs):
# fig, ax = plt.subplots(1, 1, figsize=(8, 8))
# pos = nx.spring_layout(g, k=2, **kwargs)
# labels = dict(g.nodes('name'))
# nx.draw(g, labels=labels, pos=pos, ax=ax,
# font_size=12, node_color='skyblue')
iris = load_iris()
X = iris.data
y = iris.target
x, mean, std = standardize(X)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
net = MLP([4, 'bn', 'relu', 10, 'bn', 'relu', 3])
optimizer = SGD(net.parameters(), lr=0.03, momentum=0.9)
epochs = 50
for epoch in range(epochs):
batches = split(x_train, y_train, 8)
for batch in batches:
inputs, target = batch
inputs = H.tensor(inputs)
net.zero_grad()
output = net(inputs)
loss = H.CrossEntropyLoss(output, target)
loss.backward()
optimizer.step()
train_acc, train_loss = evaluate(net, x_train, y_train)
test_acc, test_loss = evaluate(net, x_test, y_test)
print("%d Epochs." % epochs)
print("Training set:")
print("Loss: %.4f Accuracy: %.2f" % (train_loss, train_acc))
print("Test set:")
print("Loss: %.4f Accuracy: %.2f" % (test_loss, test_acc))