d_model(每个单词被映射为的向量的维度):10 heads(多头注意力机制的头数):5 d_k(每个头的特征数):2 1、输入形状为:[seq_len, batch_size, d_model] input_tensor = torch.randn(5,2,10) input_tensor表示输入到模型的张量,输入形状为:[seq_len, batch_size, d_model]。 input_tensor# 输出结果''...
这里将batch和seq_len维度打平# 再把batch=1添加到最前面的维度(为了和y做MSE)# [batch=1,seq_len,hidden_len]->[seq_len,hidden_len]out=out.view(-1,hidden_size)# [seq_len,hidden_len]->[seq_len,output_size=1]out=self.linear(out)# [seq_len,output_size=1]->[batch...
layersdefbuild_model(neurons,dropout):model=Sequential([layers.LSTM(units=neurons,input_shape=train_dataset.shape[-2:],return_sequences=True),# units=256表示有256个神经元;return_sequences=True表示将结果传到下一步layers.Dropout(dropout),# 表示删除一些神经元layers.LSTM(units=256,return_sequences=...
seq_len = q.shape[1] batch_prompt = b // len(cond_or_uncond) out = optimized_attention(q, k, v, extra_options["n_heads"]) _, _, oh, ow = extra_options["original_shape"] for weight, cond, cond_alt, uncond, ipadapter, mask, weight_type, sigma_start, sigma_end, unfold...
是指在使用BERT模型进行预测时,只输入一个样本而不是一批样本进行推断。 BERT(Bidirectional Encoder Representations from Transformers)是...
# 定义序列分类和词分类的数据集 from paddle.io import Dataset from paddlenlp.data import Tuple, Pad, Stack import paddlenlp import random import numpy as np class RealDataset(Dataset): def __init__(self, data, label, tokenizer, max_seq_len=512, for_test=False): super().__init__() ...
# 如果ws是窗口大小,那么(seq,labels)图元的总数将是len(series)-ws。definput_data(seq,ws):out=[]L=len(seq)foriinrange(L-ws):window=seq[i:i+ws]label=seq[i+ws:i+ws+1]out.append((window,label))returnout# The length of x = 800# The length of train_set = 800 - 40 = 760# ...
d_model / dec_config.decoder_attention_heads) decoder_input_pad = torch.zeros((batch_size - current_batch_size, 1), dtype=torch.long, device=model.device) if settings.RECOGNITION_STATIC_CACHE: decoder_cache = [torch.zeros((2, len(batch_images), kv_heads, settings.RECOGNITION_MAX_TOKENS,...
rotary_seq_len_interpolation_factor ... None sample_rate ... 1.0 save ... /opt/dpcvol/models/output_mixing/test_modellink11 save_interval ... 1000 scatter_gather_tensors_in_pipeline ... True seed ... 1234 seq
(self.pop*D_percent)# 预警者的人口规模占总人口规模的10%self.max_iter=max_iter# max iterself.verbose=verbose# print the result of each iter or notself.lb,self.ub=np.array(lb)*np.ones(self.n_dim),np.array(ub)*np.ones(self.n_dim)assertself.n_dim==len(self.lb)==len(self.ub)...