unsqueeze(1) else: value, index = torch.topk(decoder_output_t, 1) decoder_input = index return decoder_outputs,decoder_hidden def forward_step(self,decoder_input,decoder_hidden): embeded = self.embedding(decoder_input) out,decoder_hidden = self.gru(embeded,decoder_hidden) out = out.squeeze...
unsqueeze(1)), dim=-1) # Reshape x to (1, batch_size, embed_size+hidden_size) # print("rnn",x.size(), len(hidden_state)) out, hidden_state = self.rnn(x.transpose(0,1), hidden_state) outputs.append(out) outputs = self.dense(torch.cat(outputs, dim=0)) return outputs....
.size2",X.size()) outputs = [] for l, x in enumerate(X): # print(f"\n{l}-th token") # print("x.first.size()",x.size()) # query shape: (batch_size, 1, hidden_size) # select hidden state of the last rnn layer as query query = hidden_state[0][-1].unsqueeze(1) #...
dec_X=torch.unsqueeze(torch.tensor( [tgt_vocab['<bos>']], dtype=torch.long, device=device), dim=0) output_seq, attention_weight_seq=[], [] for_inrange(num_steps): Y, dec_state=net.decoder(dec_X, dec_stat...
class ConvAI2(Dataset): def __init__(self, dataset): self.dataset = dataset def __getitem__(self, idx): return self.dataset[idx] def __len__(self): return len(self.dataset) def _tokenized(self, seq, dialog_dict): ret = [] for w in seq.split(): try: ret.append(dialog_dict...
unsqueeze(-1) # [batch_size,1] # print('user_teacher_decoder_input size:', decoder_input.size()) else: # 不使用 teacher forcing for t in range(config.chatbot_target_max_len + 1): # +1 是因为 add_eos 加了1 decoder_outputs_t, decoder_hidden = self.forward_step(decoder_input, ...
Seq2Seq网络即sequence to sequence,序列到序列网络,输入一个序列,输出另一个序列。这个架构重要之处在于,输入序列和输出序列的长度是可变的。 Seq2Seq使用的具体方法基本都属于编码器-解码器架构。 其核心思想是: 通过编码器(Encoder)将输入序列编码成一个定长的向量表示,也称为具有上下文信息的表示,简称为上下文(...
#获取数据加载器的函数#将输入的一个batch的dialog转换成id序列,填充pad,并返回训练可用的id张量和maskdefDataLoader(pairs, voc, batch_size, shuffle=True):ifshuffle: random.shuffle(pairs) batch=[]foridx, pairinenumerate(pairs): batch.append([pair[0], pair[1]])#数据数量到达batch_size就yield出去并...
Seq2Seq模型图 Teacher Forcing 以翻译为例 之前的弊端 Teacher Forcing的论文 环境配置 代码结构 process.py load_data.py 构建分词函数tokenizer 构建数据预处理格式(Field) 载入数据(TabularDataset) 构建词表(build_vocab) 构建数据迭代器(BucketIterator) ...
# unsqueeze之后[batch_size,1,hidden_size] # transpose_y=True,后两维转置 [batch_size,hidden_size,time_steps] # matmul之后的 形状 [batch_size,1,time_steps] a=paddle.unsqueeze(decoder_hidden_h,[1]) # print(a.shape) # print(encoder_output.shape) attn_scores=paddle.matmul(a,encoder_outpu...