DataFeeder¶

class paddle.fluid.data_feeder. DataFeeder ( feed_list, place, program=None ) [source]

Api_attr

Static Graph

DataFeeder converts the data that returned by a reader into a data structure that can feed into Executor. The reader is usually a python generator that returns a list of mini-batch data entries.

Parameters

• feed_list (list) – Variables or names of Variables that need to feed.

• place (api_fluid_CPUPlace | api_fluid_CUDAPlace) – place indicates the device (CPU | GPU) the data will be fed into, if you want to feed data into GPU, please using fluid.CUDAPlace(i) (i represents the GPU id), or if you want to feed data into CPU, please using fluid.CPUPlace().

• program (api_fluid_Program , optional) – The Program that will feed data into, if program is None, it will use default_main_program(). Default None.

Raises

ValueError –

Example

import numpy as np
import paddle
import paddle.fluid as fluid

place = fluid.CPUPlace()
def reader():
    for _ in range(4):
        yield np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32'),

main_program = fluid.Program()
startup_program = fluid.Program()

with fluid.program_guard(main_program, startup_program):
    data_1 = paddle.static.data(name='data_1', shape=[None, 2, 2], dtype='float32')
    data_2 = paddle.static.data(name='data_2', shape=[None, 1, 3], dtype='float32')
    out = paddle.static.nn.fc(x=[data_1, data_2], size=2)
    # ...
feeder = fluid.DataFeeder([data_1, data_2], place)

exe = fluid.Executor(place)
exe.run(startup_program)

feed_data = feeder.feed(reader())

# print feed_data to view feed results
# print(feed_data['data_1'])
# print(feed_data['data_2'])

outs = exe.run(program=main_program,
                feed=feed_data,
                fetch_list=[out])
print(outs)

feed ( iterable )

feed¶

According to feed_list of DataFeeder and iterable , converts the input into a data structure that can feed into Executor.

Parameters

iterable (generator) – user defined python generator to read the raw input data

Returns

a dict that contains (variable name - converted tensor) pairs

Return type

dict

Example

# In this example, reader - generator will return a list of ndarray of 3 elements
# feed API will convert each ndarray input into a tensor
# the return result is a dict with keys: data_1, data_2, data_3
# result['data_1']  a LoD-Tensor with shape of  [5, 2, 1, 3]. 5 is batch size, and [2, 1, 3] is the real shape of data_1.
# result['data_2'], result['data_3'] are similar.
import numpy as np
import paddle.fluid as fluid

def reader(limit=5):
    for i in range(1, limit + 1):
        yield np.ones([6]).astype('float32') * i , np.ones([1]).astype('int64') * i, np.random.random([9]).astype('float32')

data_1 = paddle.static.data(name='data_1', shape=[None, 2, 1, 3])
data_2 = paddle.static.data(name='data_2', shape=[None, 1], dtype='int64')
data_3 = paddle.static.data(name='data_3', shape=[None, 3, 3], dtype='float32')
feeder = fluid.DataFeeder(['data_1','data_2', 'data_3'], fluid.CPUPlace())


result = feeder.feed(reader())
print(result['data_1'])
print(result['data_2'])
print(result['data_3'])