本文整理匯總了Python中paddle.batch方法的典型用法代碼示例。如果您正苦於以下問題：Python paddle.batch方法的具體用法？Python paddle.batch怎麽用？Python paddle.batch使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類paddle的用法示例。
在下文中一共展示了paddle.batch方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: infer
# 需要導入模塊: import paddle [as 別名]
# 或者: from paddle import batch [as 別名]
def infer(save_dirname=None):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
[inference_program, feed_target_names, fetch_targets] = (
fluid.io.load_inference_model(save_dirname, exe))
test_reader = paddle.batch(paddle.dataset.uci_housing.test(), batch_size=20)
test_data = six.next(test_reader())
test_feat = numpy.array(list(map(lambda x: x[0], test_data))).astype("float32")
test_label = numpy.array(list(map(lambda x: x[1], test_data))).astype("float32")
results = exe.run(inference_program,
feed={feed_target_names[0]: numpy.array(test_feat)},
fetch_list=fetch_targets)
print("infer results: ", results[0])
print("ground truth: ", test_label)
# Run train and infer. 示例2: train
# 需要導入模塊: import paddle [as 別名]
# 或者: from paddle import batch [as 別名]
def train(save_dirname):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict, avg_cost = net(x, y)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.reader.shuffle(paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=20)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
def train_loop(main_program):
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe.run(fluid.default_startup_program())
PASS_NUM = 1000
for pass_id in range(PASS_NUM):
total_loss_pass = 0
for data in train_reader():
avg_loss_value, = exe.run(
main_program, feed=feeder.feed(data), fetch_list=[avg_cost])
total_loss_pass += avg_loss_value
if avg_loss_value < 5.0:
if save_dirname is not None:
fluid.io.save_inference_model(
save_dirname, ['x'], [y_predict], exe)
return
print("Pass %d, total avg cost = %f" % (pass_id, total_loss_pass))
train_loop(fluid.default_main_program())
# Infer by using provided test data. 示例3: cosine_decay_with_warmup
# 需要導入模塊: import paddle [as 別名]
# 或者: from paddle import batch [as 別名]
def cosine_decay_with_warmup(learning_rate, step_each_epoch, epochs=120):
"""Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
decrease lr for every mini-batch and start with warmup.
"""
from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
global_step = _decay_step_counter()
lr = fluid.layers.tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
warmup_epoch = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=float(5), force_cpu=True)
epoch = ops.floor(global_step / step_each_epoch)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(epoch < warmup_epoch):
decayed_lr = learning_rate * (global_step /
(step_each_epoch * warmup_epoch))
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
with switch.default():
decayed_lr = learning_rate * \
(ops.cos((global_step - warmup_epoch * step_each_epoch) * (math.pi / (epochs * step_each_epoch))) + 1)/2
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
return lr