本文整理匯總了Python中torch.nn.Softsign方法的典型用法代碼示例。如果您正苦於以下問題：Python nn.Softsign方法的具體用法？Python nn.Softsign怎麽用？Python nn.Softsign使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類torch.nn的用法示例。
在下文中一共展示了nn.Softsign方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: get_activation
# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Softsign [as 別名]
def get_activation(act):
"""Get the activation based on the act string
Parameters
----------
act: str or callable function
Returns
-------
ret: callable function
"""
if act is None:
return lambda x: x
if isinstance(act, str):
if act == 'leaky':
return nn.LeakyReLU(0.1)
elif act == 'relu':
return nn.ReLU()
elif act == 'tanh':
return nn.Tanh()
elif act == 'sigmoid':
return nn.Sigmoid()
elif act == 'softsign':
return nn.Softsign()
else:
raise NotImplementedError
else:
return act 示例2: create_str_to_activations_converter
# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Softsign [as 別名]
def create_str_to_activations_converter(self):
"""Creates a dictionary which converts strings to activations"""
str_to_activations_converter = {"elu": nn.ELU(), "hardshrink": nn.Hardshrink(), "hardtanh": nn.Hardtanh(),
"leakyrelu": nn.LeakyReLU(), "logsigmoid": nn.LogSigmoid(), "prelu": nn.PReLU(),
"relu": nn.ReLU(), "relu6": nn.ReLU6(), "rrelu": nn.RReLU(), "selu": nn.SELU(),
"sigmoid": nn.Sigmoid(), "softplus": nn.Softplus(), "logsoftmax": nn.LogSoftmax(),
"softshrink": nn.Softshrink(), "softsign": nn.Softsign(), "tanh": nn.Tanh(),
"tanhshrink": nn.Tanhshrink(), "softmin": nn.Softmin(), "softmax": nn.Softmax(dim=1),
"none": None}
return str_to_activations_converter 示例3: __init__
# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Softsign [as 別名]
def __init__(self, n_heads=3,
layer_channels=DEFAULT_LAYERS_PARAMS,
pre_conv_channels=[64, 32, 16, 8, 4],
pre_residuals=64,
up_residuals=0,
post_residuals=3):
super(CNNVocoder, self).__init__()
self.head = Head(layer_channels,
pre_conv_channels=pre_conv_channels,
pre_residuals=pre_residuals, up_residuals=up_residuals,
post_residuals=post_residuals)
self.linear = nn.Linear(layer_channels[-1], 1)
self.act_fn = nn.Softsign() 示例4: __init__
# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Softsign [as 別名]
def __init__(self, opt):
super(RewardModel, self).__init__()
self.vocab_size = opt.vocab_size
self.word_embed_dim = 300
self.feat_size = opt.feat_size
self.kernel_num = 512
self.kernels = [2, 3, 4, 5]
self.out_dim = len(self.kernels) * self.kernel_num + self.word_embed_dim
self.emb = nn.Embedding(self.vocab_size, self.word_embed_dim)
self.emb.weight.data.copy_(torch.from_numpy(np.load("VIST/embedding.npy")))
self.proj = nn.Linear(self.feat_size, self.word_embed_dim)
self.convs = [nn.Conv2d(1, self.kernel_num, (k, self.word_embed_dim)) for k in self.kernels]
self.dropout = nn.Dropout(opt.dropout)
self.fc = nn.Linear(self.out_dim, 1, bias=True)
if opt.activation.lower() == "linear":
self.activation = None
elif opt.activation.lower() == "sign":
self.activation = nn.Softsign()
elif self.activation.lower() == "tahn":
self.activation = nn.Tanh() 示例5: get_activation_fn
# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Softsign [as 別名]
def get_activation_fn(name):
""" PyTorch built-in activation functions """
activation_functions = {
"linear": lambda: lambda x: x,
"relu": nn.ReLU,
"relu6": nn.ReLU6,
"elu": nn.ELU,
"prelu": nn.PReLU,
"leaky_relu": nn.LeakyReLU,
"threshold": nn.Threshold,
"hardtanh": nn.Hardtanh,
"sigmoid": nn.Sigmoid,
"tanh": nn.Tanh,
"log_sigmoid": nn.LogSigmoid,
"softplus": nn.Softplus,
"softshrink": nn.Softshrink,
"softsign": nn.Softsign,
"tanhshrink": nn.Tanhshrink,
}
if name not in activation_functions:
raise ValueError(
f"'{name}' is not included in activation_functions. use below one. \n {activation_functions.keys()}"
)
return activation_functions[name]