Datasets:

code-philia
/

mtpnet_text

# coding: UTF-8
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


class Config(object):

    """配置参数"""
    def __init__(self, dataset, embedding):
        self.model_name = 'TextRNN_Att'
        self.train_path = dataset + '/data/train.txt'                                # 训练集
        self.dev_path = dataset + '/data/dev.txt'                                    # 验证集
        self.test_path = dataset + '/data/test.txt'                                  # 测试集
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt', encoding='utf-8').readlines()]              # 类别名单
        self.vocab_path = dataset + '/data/vocab.pkl'                                # 词表
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'        # 模型训练结果
        self.log_path = dataset + '/log/' + self.model_name
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32'))\
            if embedding != 'random' else None                                       # 预训练词向量
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')   # 设备

        self.dropout = 0.5                                              # 随机失活
        self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)                         # 类别数
        self.n_vocab = 0                                                # 词表大小，在运行时赋值
        self.num_epochs = 10                                            # epoch数
        self.batch_size = 128                                           # mini-batch大小
        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 1e-3                                       # 学习率
        self.embed = self.embedding_pretrained.size(1)\
            if self.embedding_pretrained is not None else 300           # 字向量维度, 若使用了预训练词向量，则维度统一
        self.hidden_size = 128                                          # lstm隐藏层
        self.num_layers = 2                                             # lstm层数
        self.hidden_size2 = 64


'''Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification'''


class TextRNN_Att(nn.Module):
    def __init__(self, config):
        super(TextRNN_Att, self).__init__()
        if config.embedding_pretrained is not None:
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,
                            bidirectional=True, batch_first=True, dropout=config.dropout)
        self.tanh1 = nn.Tanh()
        # self.u = nn.Parameter(torch.Tensor(config.hidden_size * 2, config.hidden_size * 2))
        self.w = nn.Parameter(torch.zeros(config.hidden_size * 2))
        self.tanh2 = nn.Tanh()
        self.fc1 = nn.Linear(config.hidden_size * 2, config.hidden_size2)
        self.fc = nn.Linear(config.hidden_size2, config.num_classes)

    def forward(self, x):
        x, _ = x
        emb = self.embedding(x)  # [batch_size, seq_len, embeding]=[128, 32, 300]
        H, _ = self.lstm(emb)  # [batch_size, seq_len, hidden_size * num_direction]=[128, 32, 256]

        M = self.tanh1(H)  # [128, 32, 256]
        # M = torch.tanh(torch.matmul(H, self.u))
        alpha = F.softmax(torch.matmul(M, self.w), dim=1).unsqueeze(-1)  # [128, 32, 1]
        out = H * alpha  # [128, 32, 256]
        out = torch.sum(out, 1)  # [128, 256]
        out = F.relu(out)
        out = self.fc1(out)
        out = self.fc(out)  # [128, 64]
        return out
    
    def feature(self, x):
        """
        提取中间层特征向量，用于可视化
        返回attention加权后的LSTM特征（fc1层的输出）
        """
        with torch.no_grad():
            x, _ = x
            emb = self.embedding(x)  # [batch_size, seq_len, embeding]
            H, _ = self.lstm(emb)  # [batch_size, seq_len, hidden_size * num_direction]

            M = self.tanh1(H)  # [128, 32, 256]
            alpha = F.softmax(torch.matmul(M, self.w), dim=1).unsqueeze(-1)  # [128, 32, 1]
            out = H * alpha  # [128, 32, 256]
            out = torch.sum(out, 1)  # [128, 256]
            out = F.relu(out)
            return out.cpu().numpy()
    
    def get_prediction(self, x):
        """
        获取模型最终层输出向量（logits）
        """
        with torch.no_grad():
            x, _ = x
            emb = self.embedding(x)
            H, _ = self.lstm(emb)

            M = self.tanh1(H)
            alpha = F.softmax(torch.matmul(M, self.w), dim=1).unsqueeze(-1)
            out = H * alpha
            out = torch.sum(out, 1)
            out = F.relu(out)
            out = self.fc1(out)
            predictions = self.fc(out)  # [batch_size, num_classes]
            return predictions.cpu().numpy()
    
    def prediction(self, features):
        """
        根据中间特征向量预测结果
        features: 来自feature()函数的输出 [batch_size, hidden_size2]
        """
        with torch.no_grad():
            features_tensor = torch.tensor(features, dtype=torch.float32).to(next(self.parameters()).device)
            out = self.fc1(features_tensor)
            predictions = self.fc(out)  # 直接通过最后的分类层
            return predictions.cpu().numpy()

	# coding: UTF-8
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import numpy as np


	class Config(object):

	"""配置参数"""
	def __init__(self, dataset, embedding):
	self.model_name = 'TextRNN_Att'
	self.train_path = dataset + '/data/train.txt' # 训练集
	self.dev_path = dataset + '/data/dev.txt' # 验证集
	self.test_path = dataset + '/data/test.txt' # 测试集
	self.class_list = [x.strip() for x in open(
	dataset + '/data/class.txt', encoding='utf-8').readlines()] # 类别名单
	self.vocab_path = dataset + '/data/vocab.pkl' # 词表
	self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt' # 模型训练结果
	self.log_path = dataset + '/log/' + self.model_name
	self.embedding_pretrained = torch.tensor(
	np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32'))\
	if embedding != 'random' else None # 预训练词向量
	self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # 设备

	self.dropout = 0.5 # 随机失活
	self.require_improvement = 1000 # 若超过1000batch效果还没提升，则提前结束训练
	self.num_classes = len(self.class_list) # 类别数
	self.n_vocab = 0 # 词表大小，在运行时赋值
	self.num_epochs = 10 # epoch数
	self.batch_size = 128 # mini-batch大小
	self.pad_size = 32 # 每句话处理成的长度(短填长切)
	self.learning_rate = 1e-3 # 学习率
	self.embed = self.embedding_pretrained.size(1)\
	if self.embedding_pretrained is not None else 300 # 字向量维度, 若使用了预训练词向量，则维度统一
	self.hidden_size = 128 # lstm隐藏层
	self.num_layers = 2 # lstm层数
	self.hidden_size2 = 64


	'''Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification'''


	class TextRNN_Att(nn.Module):
	def __init__(self, config):
	super(TextRNN_Att, self).__init__()
	if config.embedding_pretrained is not None:
	self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
	else:
	self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
	self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,
	bidirectional=True, batch_first=True, dropout=config.dropout)
	self.tanh1 = nn.Tanh()
	# self.u = nn.Parameter(torch.Tensor(config.hidden_size * 2, config.hidden_size * 2))
	self.w = nn.Parameter(torch.zeros(config.hidden_size * 2))
	self.tanh2 = nn.Tanh()
	self.fc1 = nn.Linear(config.hidden_size * 2, config.hidden_size2)
	self.fc = nn.Linear(config.hidden_size2, config.num_classes)

	def forward(self, x):
	x, _ = x
	emb = self.embedding(x) # [batch_size, seq_len, embeding]=[128, 32, 300]
	H, _ = self.lstm(emb) # [batch_size, seq_len, hidden_size * num_direction]=[128, 32, 256]

	M = self.tanh1(H) # [128, 32, 256]
	# M = torch.tanh(torch.matmul(H, self.u))
	alpha = F.softmax(torch.matmul(M, self.w), dim=1).unsqueeze(-1) # [128, 32, 1]
	out = H * alpha # [128, 32, 256]
	out = torch.sum(out, 1) # [128, 256]
	out = F.relu(out)
	out = self.fc1(out)
	out = self.fc(out) # [128, 64]
	return out

	def feature(self, x):
	"""
	提取中间层特征向量，用于可视化
	返回attention加权后的LSTM特征（fc1层的输出）
	"""
	with torch.no_grad():
	x, _ = x
	emb = self.embedding(x) # [batch_size, seq_len, embeding]
	H, _ = self.lstm(emb) # [batch_size, seq_len, hidden_size * num_direction]

	M = self.tanh1(H) # [128, 32, 256]
	alpha = F.softmax(torch.matmul(M, self.w), dim=1).unsqueeze(-1) # [128, 32, 1]
	out = H * alpha # [128, 32, 256]
	out = torch.sum(out, 1) # [128, 256]
	out = F.relu(out)
	return out.cpu().numpy()

	def get_prediction(self, x):
	"""
	获取模型最终层输出向量（logits）
	"""
	with torch.no_grad():
	x, _ = x
	emb = self.embedding(x)
	H, _ = self.lstm(emb)

	M = self.tanh1(H)
	alpha = F.softmax(torch.matmul(M, self.w), dim=1).unsqueeze(-1)
	out = H * alpha
	out = torch.sum(out, 1)
	out = F.relu(out)
	out = self.fc1(out)
	predictions = self.fc(out) # [batch_size, num_classes]
	return predictions.cpu().numpy()

	def prediction(self, features):
	"""
	根据中间特征向量预测结果
	features: 来自feature()函数的输出 [batch_size, hidden_size2]
	"""
	with torch.no_grad():
	features_tensor = torch.tensor(features, dtype=torch.float32).to(next(self.parameters()).device)
	out = self.fc1(features_tensor)
	predictions = self.fc(out) # 直接通过最后的分类层
	return predictions.cpu().numpy()