因此,我们对output就没有那么关心,我们只关心提取的时序特征,因此我们更偏好于用hidden连接最后的dense层 等价性 实际上,output的最后时间维输出和最后一层的hidden是一致的,不论num_layer有多少层。 class GRUClassifier(nn.Module): def __init__(self, input_size, hidden_dim, n_classes,num_layers, d_rat...
这里将batch和seq_len维度打平# 再把batch=1添加到最前面的维度(为了和y做MSE)# [batch=1,seq_len,hidden_len]->[seq_len,hidden_len]out=out.view(-1,hidden_size)# [seq_len,hidden_len]->[seq_len,output_size=1]out=self.linear(out)# [seq_len,output_size=1]->[batch...
hidden:1x5x10 classRNN(nn.Module):def__init__(self,input_size,hidden_size,output_size):super(RNN,self).__init__()self.hidden_size=hidden_size self.i2h=nn.Linear(input_size+hidden_size,hidden_size)self.i2o=nn.Linear(input_size+hidden_size,output_size)self.softmax=nn.LogSoftmax(dim=...
rnn = nn.RNN(embed_dim, hidden_dim, batch_first=True) sents = [[1, 2, 4], [2, 3, 4]] h0 = torch.zeros(1, embeded.size(0), 8) # shape=(num_layers*num_directions, batch, hidden_dim) embeded = embedding(torch.LongTensor(sents)) out, hidden = rnn(embeded, h0) # out.sha...
RNN(input_dim,hidden_dim,num_layers ,…)– input_dim 表示输入的特征维度 –hidden_dim 表示输出的特征维度,如果没有特殊变化,相当于out –num_layers 表示网络的层数 –nonlinearity 表示选用的非线性激活函数,默认是 ‘tanh’ –bias 表示是否使用偏置,默认使用 ...
output_dim = 1 bptt_truncate = 5 min_clip_value = -10 max_clip_value = 10 然后我们将定义网络的权重: U = np.random.uniform(0, 1, (hidden_dim, T)) W = np.random.uniform(0, 1, (hidden_dim, hidden_dim)) V = np.random.uniform(0, 1, (output_dim, hidden_dim)) ...
importtorchimporttorch.nnasnnclassTextGenerator(nn.Module):def__init__(self,vocab_size,embedding_dim,hidden_dim):super(TextGenerator,self).__init__()self.embedding=nn.Embedding(vocab_size,embedding_dim)self.rnn=nn.RNN(embedding_dim,hidden_dim,batch_first=True)self.fc=nn.Linear(hidden_dim,vo...
self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)self.fc = nn.Linear(hidden_size, output_size)def forward(self, x):# x的形状为 [batch_size, timesteps, input_size]output, hidden = self.rnn(x)# 取隐藏状态的最后一个时间步 output = output[:, -1, :]output = self.fc(...
self.hidden_dim=hidden_dim self.bptt_truncate=bptt_truncate # Randomly initialize the network parameters self.U=np.random.uniform(-np.sqrt(1./word_dim),np.sqrt(1./word_dim),(hidden_dim,word_dim))self.V=np.random.uniform(-np.sqrt(1./hidden_dim),np.sqrt(1./hidden_dim),(word_dim,...
1defforward_propagation(self, x):2#The total number of time steps3T =len(x)4#During forward propagation we save all hidden states in s because need them later.5#We add one additional element for the initial hidden, which we set to 06s = np.zeros((T + 1, self.hidden_dim))7s[-1...