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...
A:seq_len就是输入的sequence_length(序列长度),既然LSTM是处理序列数据的,那么序列就需要一个长度。虽然LSTM处理的序列长度通常是不固定的,但是Pytorch和TensorFlow的集成实现还是固定了input的序列长度,在处理不固定长度的数据(如机器翻译),通常加入开始和结束符号并采用序列的最大长度MAX_LEN作为seq_len 来看几个实际...
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 ...
38 mask_batch=torch.zeros((batch_len,max_seq_length)) 39 fea_batch=[] 40 label_batch=[] 41 id_batch=[] 42 for i in range(len(batch_dic)): 43 dic=batch_dic[i] 44 fea_batch.append(dic['feature']) 45 label_batch.append(dic['label']) ...
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_...
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) ...
- 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) ...
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...
batch_size = 2 max_length = 3 hidden_size = 2 n_layers = 2 # 这个RNN由两个全连接层组成,对应的两个hidden_state的维度是2,输入向量维度是1 rnn = nn.RNN(1, hidden_size, n_layers, batch_first=True) 然后,假设我们的输入数据是这样子的: x = torch.FloatTensor([[1, 0, 0], [1, 2...