def main():
device = set_device(FLAGS.device)
fluid.enable_dygraph(device) if FLAGS.dynamic else None
train_dataset = MnistDataset(mode='train')
val_dataset = MnistDataset(mode='test')
inputs = [Input([None, 784], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
model = MNIST()
optim = Momentum(learning_rate=FLAGS.lr,
momentum=.9,
parameter_list=model.parameters())
model.prepare(optim,
CrossEntropy(),
Accuracy(topk=(1, 2)),
inputs,
labels,
device=FLAGS.device)
if FLAGS.resume is not None:
model.load(FLAGS.resume)
model.fit(train_dataset,
val_dataset,
epochs=FLAGS.epoch,
batch_size=FLAGS.batch_size,
save_dir='mnist_checkpoint')
def main():
paddle.enable_static() if FLAGS.static else None
device = paddle.set_device(FLAGS.device)
train_dataset = MNIST(mode='train')
val_dataset = MNIST(mode='test')
inputs = [Input(shape=[None, 1, 28, 28], dtype='float32', name='image')]
labels = [Input(shape=[None, 1], dtype='int64', name='label')]
net = LeNet()
model = paddle.Model(net, inputs, labels)
optim = Momentum(learning_rate=FLAGS.lr,
momentum=.9,
parameter_list=model.parameters())
model.prepare(optim, paddle.nn.CrossEntropyLoss(),
paddle.metric.Accuracy(topk=(1, 2)))
if FLAGS.resume is not None:
model.load(FLAGS.resume)
if FLAGS.eval_only:
model.evaluate(val_dataset, batch_size=FLAGS.batch_size)
return
model.fit(train_dataset,
val_dataset,
epochs=FLAGS.epoch,
batch_size=FLAGS.batch_size,
save_dir=FLAGS.output_dir)
def main():
@contextlib.contextmanager
def null_guard():
yield
guard = fluid.dygraph.guard() if FLAGS.dynamic else null_guard()
if not os.path.exists('mnist_checkpoints'):
os.mkdir('mnist_checkpoints')
train_loader = fluid.io.xmap_readers(
lambda b: [
np.array([x[0] for x in b]).reshape(-1, 1, 28, 28),
np.array([x[1] for x in b]).reshape(-1, 1)
],
paddle.batch(fluid.io.shuffle(paddle.dataset.mnist.train(), 6e4),
batch_size=FLAGS.batch_size,
drop_last=True), 1, 1)
val_loader = fluid.io.xmap_readers(
lambda b: [
np.array([x[0] for x in b]).reshape(-1, 1, 28, 28),
np.array([x[1] for x in b]).reshape(-1, 1)
],
paddle.batch(paddle.dataset.mnist.test(),
batch_size=FLAGS.batch_size,
drop_last=True), 1, 1)
device_ids = list(range(FLAGS.num_devices))
with guard:
model = MNIST()
optim = Momentum(learning_rate=FLAGS.lr,
momentum=.9,
parameter_list=model.parameters())
model.prepare(optim, CrossEntropy())
if FLAGS.resume is not None:
model.load(FLAGS.resume)
for e in range(FLAGS.epoch):
train_loss = 0.0
train_acc = 0.0
val_loss = 0.0
val_acc = 0.0
print("======== train epoch {} ========".format(e))
for idx, batch in enumerate(train_loader()):
outputs, losses = model.train(batch[0],
batch[1],
device='gpu',
device_ids=device_ids)
acc = accuracy(outputs[0], batch[1])[0]
train_loss += np.sum(losses)
train_acc += acc
if idx % 10 == 0:
print("{:04d}: loss {:0.3f} top1: {:0.3f}%".format(
idx, train_loss / (idx + 1), train_acc / (idx + 1)))
print("======== eval epoch {} ========".format(e))
for idx, batch in enumerate(val_loader()):
outputs, losses = model.eval(batch[0],
batch[1],
device='gpu',
device_ids=device_ids)
acc = accuracy(outputs[0], batch[1])[0]
val_loss += np.sum(losses)
val_acc += acc
if idx % 10 == 0:
print("{:04d}: loss {:0.3f} top1: {:0.3f}%".format(
idx, val_loss / (idx + 1), val_acc / (idx + 1)))
model.save('mnist_checkpoints/{:02d}'.format(e))