pytorch 中的 torch.nn.RNN 的参数 1、定义RNN的网络结构的参数(类似于CNN中定义 in_channel,out_channel,kernel_size等等) input_size 输入x的特征大小(以mnist图像为例,特征大小为28*28 = 784) hidden_...查看原文pytorch nn.Conv2d 参数 nn.Conv2d(self, in_channels, out_channels, kernel_size, ...
Example >>> from torch.nn.utils.rnn import pack_sequence >>> a = torch.tensor([1,2,3]) >>> b = torch.tensor([4,5]) >>> c = torch.tensor([6]) >>> pack_sequence([a, b, c]) PackedSequence(data=tensor([ 1, 4, 6, 2, 5, 3]), batch_sizes=tensor([ 3, 2, 1]))...
Example #2Source File: neural_networks.py From pase with MIT License 6 votes def __init__(self, options,inp_dim): super(RNN_cudnn, self).__init__() self.input_dim=inp_dim self.hidden_size=int(options['hidden_size']) self.num_layers=int(options['num_layers']) self.nonlinearity...
value (Number): the number to be added to each element of :attr:`input` Keyword arguments: out (Tensor, optional): the output tensor. Example:: >>> a = torch.randn(4) >>> a tensor([ 0.0202, 1.0985, 1.3506, -0.6056]) >>> torch.add(a, 20) ... [-18.6971, -18.0736, -17.0994...
classtorch.nn.RNN(args, kwargs)* nn.Parameter 参考PyTorch里面的torch.nn.Parameter(): 【对于self.v = torch.nn.Parameter(torch.FloatTensor(hidden_size)), 可以把这个函数理解为类型转换函数,将一个不可训练的类型Tensor转换成可以训练的类型parameter并将这个parameter绑定到这个module里面(net.parameter()中...
Example #26Source File: rnn.py From hgraph2graph with MIT License 5 votes def forward(self, fmess, bgraph): h = torch.zeros(fmess.size(0), self.hidden_size, device=fmess.device) c = torch.zeros(fmess.size(0), self.hidden_size, device=fmess.device) mask = torch.ones(h.size(0)...
To get intuitions about the algorithm I will try to explain it with an example. The example is a set of data on Employee Satisfaction and Salary level. 为了获得有关算法的直觉,我将尝试通过一个示例进行解释。 该示例是一组有关员工满意度和薪资水平的数据。
and will appear e.g. inparameters()iterator. Assigning a Tensor doesn’t have such effect. This is because one might want to cache some temporary state, like last hidden state of the RNN, in the model. If there was no such class asParameter, these temporaries would get registered too. ...
rnn = nn.GRU(hidden_size + embed_size, hidden_size, num_layers=n_layer, dropout=(0 if n_layer == 1 else dropout)) self.out = nn.Linear(hidden_size, output_size) self.init_weight() self.self_attention = nn.MultiheadAttention(hidden_size, 8) ...
sequence-to-one.lua: a simple sequence-to-one example that usesRecurrenceto build an RNN andSelectTable(-1)to select the last time-step for discriminating the sequence. encoder-decoder-coupling.lua: uses two stacks ofnn.SeqLSTMto implement an encoder and decoder. The final hidden state of ...