From 96bfbf19fbbcd1318a306da6ceca0227296f2547 Mon Sep 17 00:00:00 2001 From: xuyige Date: Tue, 23 Apr 2019 20:42:41 +0800 Subject: [PATCH] update documents in MLP --- fastNLP/modules/decoder/MLP.py | 30 +++++++++++++++++++++++++----- 1 file changed, 25 insertions(+), 5 deletions(-) diff --git a/fastNLP/modules/decoder/MLP.py b/fastNLP/modules/decoder/MLP.py index 3a793f24..e7fafd68 100644 --- a/fastNLP/modules/decoder/MLP.py +++ b/fastNLP/modules/decoder/MLP.py @@ -7,17 +7,33 @@ from fastNLP.modules.utils import initial_parameter class MLP(nn.Module): """Multilayer Perceptrons as a decoder - :param list size_layer: list of int, define the size of MLP layers. layer的层数为 len(size_layer) - 1 - :param str or list activation: str or function or a list, the activation function for hidden layers. - :param str or function output_activation : str or function, the activation function for output layer - :param str initial_method: the name of initialization method. - :param float dropout: the probability of dropout. + :param list size_layer: 一个int的列表，用来定义MLP的层数，列表中的数字为每一层是hidden数目。MLP的层数为 len(size_layer) - 1 + :param str or list activation: + 一个字符串或者函数或者字符串跟函数的列表，用来定义每一个隐层的激活函数，字符串包括relu，tanh和sigmoid，默认值为relu + :param str or function output_activation : 字符串或者函数，用来定义输出层的激活函数，默认值为None，表示输出层没有激活函数 + :param str initial_method: 参数初始化方式 + :param float dropout: dropout概率，默认值为0 .. note:: 隐藏层的激活函数通过activation定义。一个str/function或者一个str/function的list可以被传入activation。 如果只传入了一个str/function，那么所有隐藏层的激活函数都由这个str/function定义； 如果传入了一个str/function的list，那么每一个隐藏层的激活函数由这个list中对应的元素定义，其中list的长度为隐藏层数。 输出层的激活函数由output_activation定义，默认值为None，此时输出层没有激活函数。 + + Examples:: + + >>> net1 = MLP([5, 10, 5]) + >>> net2 = MLP([5, 10, 5], 'tanh') + >>> net3 = MLP([5, 6, 7, 8, 5], 'tanh') + >>> net4 = MLP([5, 6, 7, 8, 5], 'relu', output_activation='tanh') + >>> net5 = MLP([5, 6, 7, 8, 5], ['tanh', 'relu', 'tanh'], 'tanh') + >>> for net in [net1, net2, net3, net4, net5]: + >>> x = torch.randn(5, 5) + >>> y = net(x) + >>> print(x) + >>> print(y) + >>> + """ def __init__(self, size_layer, activation='relu', output_activation=None, initial_method=None, dropout=0.0): @@ -63,6 +79,10 @@ class MLP(nn.Module): initial_parameter(self, initial_method) def forward(self, x): + """ + :param torch.Tensor x: MLP接受的输入 + :return: torch.Tensor : MLP的输出结果 + """ for layer, func in zip(self.hiddens, self.hidden_active): x = self.dropout(func(layer(x))) x = self.output(x)