def train(dataset: 'Dataset', epochs: int = 10):
loader = DataLoader(dataset, batch_size=2, shuffle=True)
model = NNModel(n_input=2, n_output=3)
# model.to(device='cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = torch.nn.CrossEntropyLoss()
start_tm = time.time()
for epoch in range(1, epochs + 1):
train_loss = 0.0
train_acc = 0
for x, y in loader:
optimizer.zero_grad()
y_pred = model(x)
y = torch.max(torch.squeeze(y, dim=1), dim=1).indices
loss = criterion(y_pred, y)
loss.backward()
optimizer.step()
train_loss += loss.item()
train_acc += (y_pred.argmax(1) == y).sum().item()
print(f'[epoch {epoch:02d}]\tloss:{train_loss}\taccuracy:{train_acc}')
finish_tm = time.time()
print(f'train finished.({finish_tm-start_tm}sec)')
def main():
X = torch.randn(1, 1, 32, 32)
y = torch.randn(10).view(1, -1)
model = NNModel()
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
optimizer.zero_grad()
y_pred = model(X)
loss = criterion(y_pred, y)
print(y_pred)
print(loss)
loss.backward()
optimizer.step()
print('DONE')