Create hm_week8_model.py

张志/hm_week8/hm_week8_model.py

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+# -*- coding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+from torch.optim import Adam, SGD
+from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
+"""
+建立网络模型结构
+"""
+
+class SentenceEncoder(nn.Module):
+ def __init__(self, config):
+ super(SentenceEncoder, self).__init__()
+ hidden_size = config["hidden_size"]
+ vocab_size = config["vocab_size"] + 1
+ max_length = config["max_length"]
+ self.embedding = nn.Embedding(vocab_size, hidden_size, padding_idx=0)
+ # self.lstm = nn.LSTM(hidden_size, hidden_size, batch_first=True, bidirectional=True)
+ self.layer = nn.Linear(hidden_size, hidden_size)
+ self.dropout = nn.Dropout(0.5)
+
+ #输入为问题字符编码
+ def forward(self, x):
+ x = self.embedding(x)
+ #使用lstm
+ # x, _ = self.lstm(x)
+ #使用线性层
+ x = self.layer(x)
+ x = nn.functional.max_pool1d(x.transpose(1, 2), x.shape[1]).squeeze()
+ return x
+
+
+class SiameseNetwork(nn.Module):
+ def __init__(self, config):
+ super(SiameseNetwork, self).__init__()
+ self.sentence_encoder = SentenceEncoder(config)
+ self.loss = nn.CosineEmbeddingLoss()
+
+ # 计算余弦距离 1-cos(a,b)
+ # cos=1时两个向量相同，余弦距离为0；cos=0时，两个向量正交，余弦距离为1
+ def cosine_distance(self, tensor1, tensor2):
+ tensor1 = torch.nn.functional.normalize(tensor1, dim=-1)
+ tensor2 = torch.nn.functional.normalize(tensor2, dim=-1)
+ cosine = torch.sum(torch.mul(tensor1, tensor2), axis=-1)
+ return 1 - cosine
+
+ def cosine_triplet_loss(self, a, p, n, margin=None):
+ ap = self.cosine_distance(a, p)
+ an = self.cosine_distance(a, n)
+ if margin is None:
+ diff = ap - an + 0.1
+ else:
+ diff = ap - an + margin.squeeze()
+ return torch.mean(diff[diff.gt(0)]) #greater than
+
+ # #sentence : (batch_size, max_length)
+ # def forward(self, sentence1, sentence2=None, target=None):
+ # #同时传入两个句子
+ # if sentence2 is not None:
+ # vector1 = self.sentence_encoder(sentence1) #vec:(batch_size, hidden_size)
+ # vector2 = self.sentence_encoder(sentence2)
+ # #如果有标签，则计算loss
+ # if target is not None:
+ # return self.loss(vector1, vector2, target.squeeze())
+ # #如果无标签，计算余弦距离
+ # else:
+ # return self.cosine_distance(vector1, vector2)
+ # #单独传入一个句子时，认为正在使用向量化能力
+ # else:
+ # return self.sentence_encoder(sentence1)
+
+ def forward(self, sentence1, sentence2=None, sentence3=None):
+ #同时传入3个句子,则做tripletloss的loss计算
+ if sentence2 is not None and sentence3 is not None:
+ vector1 = self.sentence_encoder(sentence1)
+ vector2 = self.sentence_encoder(sentence2)
+ vector3 = self.sentence_encoder(sentence3)
+ return self.cosine_triplet_loss(vector1, vector2, vector3)
+ #单独传入一个句子时，认为正在使用向量化能力
+ else:
+ return self.sentence_encoder(sentence1)
+
+def choose_optimizer(config, model):
+ optimizer = config["optimizer"]
+ learning_rate = config["learning_rate"]
+ if optimizer == "adam":
+ return Adam(model.parameters(), lr=learning_rate)
+ elif optimizer == "sgd":
+ return SGD(model.parameters(), lr=learning_rate)
+
+
+if __name__ == "__main__":
+ from config import Config
+ Config["vocab_size"] = 10
+ Config["max_length"] = 4
+ model = SiameseNetwork(Config)
+ s1 = torch.LongTensor([[1,2,3,0], [2,2,0,0]])
+ s2 = torch.LongTensor([[1,2,3,4], [3,2,3,4]])
+ l = torch.LongTensor([[1],[0]])
+ y = model(s1, s2, l)
+ print(y)
+ # print(model.state_dict())