1fromtorch.nn.utils.rnnimportpad_sequence23defcollate_func(batch_dic):4batch_len=len(batch_dic) # 批尺寸5max_seq_length=max([dic['length']fordicinbatch_dic]) # 一批数据中最长的那个样本长度6mask_batch=torch.zeros((batch_len
我们可以collate_fn在DataLoader中使用 参数,该参数使我们可以定义如何在特定批次中堆叠序列。要使用此功能,我们需要定义一个函数,该函数将一个批处理作为输入并返回 基于 该批处理的填充序列长度的(x_batch, y_batch)max_sequence_length。我在以下函数中使用的函数是简单的NumPy操作。另外,该函数已正确注释,因此您...
6 mask_batch=torch.zeros((batch_len,max_seq_length)) # mask 7 fea_batch=[] 8 label_batch=[] 9 id_batch=[] 10 for i in range(len(batch_dic)): # 分别提取批样本中的feature、label、id、length信息 11 dic=batch_dic[i] 12 fea_batch.append(dic['feature']) 13 label_batch.append(...
max_length = 3 hidden_size = 2 n_layers =1 tensor_in = torch.FloatTensor([[1, 2, 3], [1, 0, 0]]).resize_(2,3,1) tensor_in = Variable( tensor_in ) #[batch, seq, feature], [2, 3, 1] seq_lengths = [3,1] # list of integers holding information about the batch size ...
max_length = 3 hidden_size = 2 n_layers =1 tensor_in = torch.FloatTensor([[1, 2, 3], [1, 0, 0]]).resize_(2,3,1) tensor_in = Variable( tensor_in ) #[batch, seq, feature], [2, 3, 1] seq_lengths = [3,1] # list of integers holding information about the batch size...
从不同长度的单词索引的多个列表用零补充到最大长度并构成一个(Max_length, Batch_size)矩阵的过程可以被Pytorch的torch.nn.utils.rnn.pad_sequence来完成。 >>>fromtorch.nn.utils.rnnimportpad_sequence>>>seq_vectors=[torch.tensor([11,12,13]),torch.tensor([21,22]),torch.tensor([31,32])]>>>pad...
self.max_length = max_length self.embedding = nn.Embedding(self.output_size, self.hidden_size) self.attn = nn.Linear(self.hidden_size * 2, self.max_length) self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size) ...
d_model = 8state_size = 128 # Example state sizeseq_len = 100 # Example sequence lengthbatch_size = 256 # Example batch sizelast_batch_size = 81 # only for the very last batch of the datasetcurrent_batch_size = batch_sizedifferent_...
input_seq: 一个batch的输入句子,shape是(max_length, batch_size) input_lengths: 一个长度为batch的list,表示句子的实际长度。 hidden: 初始化隐状态(通常是零),shape是(n_layers x num_directions, batch_size, hidden_size) 输出: outputs: 最后一层GRU的输出向量(双向的向量加在了一起),shape(max_leng...
- max_len: 句子的最大长度,默认512 形状: - 输入: [batch_size, seq_length, num_features] - 输出: [batch_size, seq_length, num_features] 例子: >>> X = torch.randn((2,4,10)) >>> X = positional_encoding(X, 10) >>> print(X.shape) ...