fromtorch.nn.utils.rnnimportpack_padded_sequence, pad_packed_sequencefromtorch.nnimportutilsasnn_utilsimporttorch.nn.functionalasFimporttorch# seq example# batch的尺寸是5,假设我们有五句话,每句话有不定长的词汇# 这里只假设每个词汇的feature是一维的batch_size =5a = torch.tensor([1,2]) b = torc...
1、去除⾮⽂本部分 2、分词 3、去除停⽤词 4、对英⽂单词进⾏词⼲提取(stemming)和词型还原(lemmatization)5、转为⼩写 6、特征处理 Bag of Words Tf-idf N-gram Word2vec 词⼲提取和词型还原 from nltk.stem import SnowballStemmer stemmer = SnowballStemmer("english") # 选择语⾔ from...
# padding padding='post'or padding='pre'--- x0=sequence.pad_sequences(x,maxlen=MaxLen,value=0,padding='post') print(x0) # 用 0填充 x1=sequence.pad_sequences(x,maxlen=MaxLen,value=0,padding='pre') print(x1) # 截断 truncating='post' or truncating='pre'--- x = [[1,2,4,5...
batch, features), not the more# familiar (batch, sequence, features), so we have to fix it.output = output.permute(1,0,2)# For some other reason, the torch transformer takes the mask backwards.mask = ~mask
_class_`torch.nn.``GRU`(_*args_, _**kwargs_)[[source]](http://pytorch.org/docs/master/_modules/torch/nn/modules/rnn.html#GRU) Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. For each element in the input sequence, each layer computes the following ...
unpacked_out_s, _ = pad_packed_sequence(out_s, batch_first=True)# Pair-wise interaction matrixI_matrix = torch.bmm(unpacked_out_s, out_a.permute(0,2,1))# Column-wise softmaxa2s_attn = F.softmax(I_matrix, dim=1)# Row-wise softmax => Column-wise average => aspect attentions2...
data.tolist(), batch_first=True) augmented_output, augmented_state = augmented_lstm(lstm_input, (initial_state, initial_memory)) pytorch_output, pytorch_state = pytorch_lstm(lstm_input, (initial_state, initial_memory)) pytorch_output_sequence, _ = pad_packed_sequence(pytorch_output, batch_...
_pack_padded_sequence _pad_packed_sequence _remove_batch_dim _reshape_from_tensor _rowwise_prune _s_where _sample_dirichlet _saturate_weight_to_fp16 _shape_as_tensor _six _sobol_engine_draw _sobol_engine_ff_ _sobol_engine_initialize_state_ _sobol_engine_scramble_ _softmax _softmax_...
在解决这个问题之前,我们需要了解torch.arange函数的基本概念和用法。torch.arange(start, end, step)函数用于生成一个从start开始、以step为步长、不包含end的等差数列。其中,start表示数列的起始值,end表示数列的结束值,step表示数列的步长。 在应用场景方面,torch.arange函数常用于生成索引、创建数据集、定义模型参数...
8、pin_memory,注释写得很清楚了: pin_memory (bool, optional): If True, the data loader will copy tensors into CUDA pinned memory before returning them. 也就是一个数据拷贝的问题。9、timeout,是用来设置数据读取的超时时间的,但超过这个时间还没读取到数据的话就会报错。 在__init__中,Random...