本文整理汇总了Python中paddle.fluid.DataFeeder方法的典型用法代码示例。如果您正苦于以下问题：Python fluid.DataFeeder方法的具体用法？Python fluid.DataFeeder怎么用？Python fluid.DataFeeder使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类paddle.fluid的用法示例。
在下文中一共展示了fluid.DataFeeder方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: train
# 需要导入模块: from paddle import fluid [as 别名]
# 或者: from paddle.fluid import DataFeeder [as 别名]
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2: evaluation
# 需要导入模块: from paddle import fluid [as 别名]
# 或者: from paddle.fluid import DataFeeder [as 别名]
def evaluation(program, reader, fetch_list, place):
feed_var_list = [
program.global_block().var('pixel'), program.global_block().var('label')
]
feeder_test = fluid.DataFeeder(feed_list=feed_var_list, place=place)
test_exe = fluid.Executor(place)
losses, accuracies = AverageMeter(), AverageMeter()
for tid, test_data in enumerate(reader()):
loss, acc = test_exe.run(program=program,
feed=feeder_test.feed(test_data),
fetch_list=fetch_list)
losses.update(float(loss), len(test_data))
accuracies.update(float(acc) * 100, len(test_data))
return losses.avg, accuracies.avg