Sequential

class paddle.nn. Sequential ( *layers ) [source]

Sequential container. Sub layers will be added to this container in the order of argument in the constructor. The argument passed to the constructor can be iterable Layers or iterable name Layer pairs.

Parameters

*layers (tuple) – Layers or iterable name Layer pairs.

Examples

import paddle
import numpy as np

data = np.random.uniform(-1, 1, [30, 10]).astype('float32')
data = paddle.to_tensor(data)
# create Sequential with iterable Layers
model1 = paddle.nn.Sequential(
    paddle.nn.Linear(10, 1), paddle.nn.Linear(1, 2)
)
model1[0]  # access the first layer
res1 = model1(data)  # sequential execution

# create Sequential with name Layer pairs
model2 = paddle.nn.Sequential(
    ('l1', paddle.nn.Linear(10, 2)),
    ('l2', paddle.nn.Linear(2, 3))
)
model2['l1']  # access l1 layer
model2.add_sublayer('l3', paddle.nn.Linear(3, 3))  # add sublayer
res2 = model2(data)  # sequential execution
forward ( input )

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments