為您找到相關(guān)結(jié)果67個(gè)
詳解PyTorch nn.Embedding() 嵌入_python_腳本之家
torch.nn.Embedding(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, _freeze=False, device=None, dtype=None) A simple lookup table that stores embeddings of a fixed dictionary and size. 一個(gè)簡(jiǎn)單的查找表,...
www.dbjr.com.cn/python/330085t...htm 2025-6-6
關(guān)于tensorflow的幾種參數(shù)初始化方法小結(jié)_python_腳本之家
const_init=tf.constant_initializer(val) embeddings=tf.get_variable("embed",shape=[5,7],dtype=tf.float32,initializer=const_init) embed=tf.nn.embedding_lookup(embeddings, train_inputs)#在embedding中查找train_input所對(duì)應(yīng)的表示 print("embed",embed) sum_embed=tf.reduce_mean(embed,1) initall=tf...
www.dbjr.com.cn/article/1778...htm 2025-5-25
python學(xué)習(xí)之基于Python的人臉識(shí)別技術(shù)學(xué)習(xí)_python_腳本之家
known_face_names=[] # Load the known faces and embeddings fornamein["person_1","person_2","person_3"]: image=face_recognition.load_image_file(f"{name}.jpg") face_encoding=face_recognition.face_encodings(image)[0] known_face_encodings.append(face_encoding) known_face_names.append(name) ...
www.dbjr.com.cn/article/2790...htm 2025-6-7
python使用RNN實(shí)現(xiàn)文本分類_python_腳本之家
use_embeddings=False,embedding_init=None): self.num_seqs = num_seqs self.num_steps = num_steps self.num_units = num_units self.num_classes = num_classes self.num_layers = num_layers self.vocab_size = vocab_size self.embedding_size = embedding_size self.use_embeddings = use_embeddings ...
www.dbjr.com.cn/article/1407...htm 2025-6-7
如何用Pytorch搭建一個(gè)房?jī)r(jià)預(yù)測(cè)模型_python_腳本之家
for i, embed in enumerate(self.embeddings) ] x = torch.cat(cat_x + [num_inputs], dim=1) x = self.hidden(x) x = self.dropout_layer(x) x = self.hidden_act(x) y_pred = self.output(x) return y_pred house_model = RegressionModel( data_ames, num_features, cat_cols ) 我們?cè)?..
www.dbjr.com.cn/article/2791...htm 2025-6-4
python使用Word2Vec進(jìn)行情感分析解析_python_腳本之家
word2vec也叫word embeddings,中文名“詞向量”,作用就是將自然語(yǔ)言中的字詞轉(zhuǎn)為計(jì)算機(jī)可以理解的稠密向量(Dense Vector)。在word2vec出現(xiàn)之前,自然語(yǔ)言處理經(jīng)常把字詞轉(zhuǎn)為離散的單獨(dú)的符號(hào),也就是One-Hot Encoder。 在語(yǔ)料庫(kù)中,杭州、上海、寧波、北京各對(duì)應(yīng)一個(gè)向量,向量中只有一個(gè)值為1,其余都為0。但是使用One...
www.dbjr.com.cn/article/1921...htm 2025-6-6