torch.nn.lstm()接受的数据输入是(序列长度,batchsize,输入维数),使用batch_first=True,可以使lstm接受维度为(batchsize,序列长度,输入维数)的数据输入,同时,lstm的输出数据维度也会变为batchsize放在第一维(可参考这篇博客)。
经过实测,发现batch_first= True要比batch_first = False更快(不知道为啥pytorch要默认是batchfirst= False,同时网上很多地方都在说batch_first= False性能更好) x_1 = torch.randn(100,200,512) x_2 = x_1.transpose(0,1) model_1 = torch.nn.LSTM(batch_first=True,hidden_size=1024,input_size=512...
batch_first=True or False输出有什么区别? 首先,LSTM默认batch_first=False,即默认batch_size这个维度是在数据维度的中间的那个维度,即喂入的数据为【seq_len, batch_size, hidden_size】这样的格式。此时 lstm_out:【seq_len, batch_size, hidden_size * num_directions】 lstm_hn:【num_directions * num_l...
## 如果在初始化LSTM时令batch_first=True,那么input和output的shape将由: ## input(seq_len, batch_size, input_size) ## output(seq_len, batch_size, num_directions * hidden_size) ## 变为 ## input(batch_size, seq_len, input_size) ## output(batch_size, seq_len, num_directions * hidden...
batch_first:True则输入输出的数据格式为 (batch, seq, feature) dropout:除最后一层,每一层的输出都进行dropout,默认为: 0 bidirectional:True则为双向lstm默认为False 结合前面的图形,我们一个个看。 (1)input_size:x的特征维度,就是数据立方体中的F,在NLP中就是一个词被embedding后的向量长度,如下图所示:...
PyTorch LSTM中的batch_first 、、 我是这个领域的新手,所以我仍然不了解PyTorch LSTM中的batch_first。我尝试了别人向我推荐的代码,当batch_first = False时,它对我的训练数据起作用,它为官方LSTM和手动LSTM产生相同的输出。但是,当我更改batch_first =True时,它不再产生相同的值,而我需要将batch_first</e ...
use_cuda = use_cuda # set option for device selection # LSTM Layer self.lstm = nn.LSTM(n_features, n_hidden, num_layers=n_lstm_layers, batch_first=True) # As we have transformed our data in this way # first dense after lstm self.fc1 = nn.Linear(n_hidden * sequence_len, n_...
output(seq_len, batch_size, num_directions * hidden_size) 1. h_n和c_n的shape保持不变,参数解释见前文。 batch_first 如果在初始化LSTM时令batch_first=True,那么input和output的shape将由: input(seq_len, batch_size, input_size) output(seq_len, batch_size, num_directions * hidden_size) ...
self.n_lstm_layers = n_lstm_layers self.nhid = n_hidden self.use_cuda = use_cuda# set option for device selection # LSTM Layer self.lstm = nn.LSTM(n_features, n_hidden, num_layers=n_lstm_layers, batch_first=True)# As we have transformed our data in this way ...
self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, output_dim) def forward(self, x): h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_() c0 = torch.zeros(self.layer_dim, x.size(0), self.hidde...