OpenKS DataLoader接口说明1

󰥵󰦾󰉁󰤨󰑳󰪭󰥂󰕾󰖰

󰥧󰦾󰤨󰑳󰋒󰆠󰐩󰤨󰑳󰦾󰮜󰉁

󰣑󰶗󰥂󰕾󰂁󰮜󰉁

decorate_sample_generator(sample_generator, batch_size, drop_last=True, places=None)

 decorate_sample_list_generator(reader, places=None) 

decorate_batch_generator(reader, places=None) 󰏠󰋒󰣑󰶗



class paddle.fluid.io.PyReader(feed_list=None, capacity=None, use_double_buffer=Tru

󰉁󰥂󰕾

󰵝󰥼󰋒󰫼󰥼󰫇󰏶󰉁󰮢󰋒

󰪭󰋒󰐏󰲖

󰋒󰦾󰫞󰥧󰦾

󰤨󰑳󰋒󰤁󰍒󰎰󰥧󰦾󰆠󰐩󰤨󰑳

󰫞󰋒󰈿󰤁󰍒󰎰

󰥂󰋒󰫍󰛲

󰥂󰋒󰫍󰋒󰢳󰢳󰋒

󰪭󰮢󰋒󰏮󰛲󰥂󰋒󰫍

󰜣󰴪󰉁󰉁

 󰋒󰦾󰣊󰤨󰑳󰤳

󰓿󰉁󰥧󰦄󰉁 start() 󰔏󰝕 Executor.run()

󰋒 fluid.core.EOFException 󰝕󰉁󰥧󰦄󰉁 reset() 󰮠󰕾



import paddle

import paddle.fluid as fluid

import numpy as np

EPOCH_NUM = 3

ITER_NUM = 5

BATCH_SIZE = 3

for i in range(ITER_NUM):

fake_image = np.random.uniform(low=0,

high=255,

size=[height, width])

fake_label = np.ones([1])

yield fake_image, fake_label

return reader

image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')

label = fluid.layers.data(name='label', shape=[1], dtype='int64')

reader = fluid.io.PyReader(feed_list=[image, label],

executor = fluid.Executor(fluid.CPUPlace())

executor.run(fluid.default_startup_program())

for i in range(EPOCH_NUM):

reader.start()

while True:

try:

executor.run(feed=None)

except fluid.core.EOFException:

reader.reset()

break

EPOCH_NUM = 3

ITER_NUM = 5

BATCH_SIZE = 10

def network(image, label):

# 󰉁󰕲󰔗softmax

predict = fluid.layers.fc(input=image, size=10, act='softmax')

return fluid.layers.cross_entropy(input=predict, label=label)

def reader_creator_random_image(height, width):

def reader():

for i in range(ITER_NUM):

reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True, ret

urn_list=False)

user_defined_reader = reader_creator_random_image(784, 784)

reader.decorate_sample_list_generator(

paddle.batch(user_defined_reader, batch_size=BATCH_SIZE),

fluid.core.CPUPlace())