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# 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_path, embedding='random'):
self.model_name = 'TextCNN'
self.train_path = dataset_path + '/train.txt'
self.dev_path = dataset_path + '/dev.txt'
self.test_path = dataset_path + '/test.txt'
self.class_list = [x.strip() for x in open(
dataset_path + '/class.txt', encoding='utf-8').readlines()]
self.vocab_path = dataset_path + '/vocab.pkl'
# 预训练词向量
if embedding != 'random':
self.embedding_pretrained = torch.tensor(
np.load(dataset_path + '/' + embedding)["embeddings"].astype('float32'))
else:
self.embedding_pretrained = None
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 模型参数
self.dropout = 0.5
self.require_improvement = 1000
self.num_classes = len(self.class_list)
self.n_vocab = 0 # 在运行时赋值
self.num_epochs = 20
self.batch_size = 128
self.pad_size = 32
self.learning_rate = 1e-3
self.embed = self.embedding_pretrained.size(1) if self.embedding_pretrained is not None else 300
self.filter_sizes = (2, 3, 4)
self.num_filters = 256
class TextCNN(nn.Module):
"""TextCNN模型"""
def __init__(self, config):
super(TextCNN, 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.convs = nn.ModuleList(
[nn.Conv2d(1, config.num_filters, (k, config.embed)) for k in config.filter_sizes])
self.dropout = nn.Dropout(config.dropout)
self.fc = nn.Linear(config.num_filters * len(config.filter_sizes), config.num_classes)
def conv_and_pool(self, x, conv):
x = F.relu(conv(x)).squeeze(3)
x = F.max_pool1d(x, x.size(2)).squeeze(2)
return x
def forward(self, x):
out = self.embedding(x[0])
out = out.unsqueeze(1)
out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
features = self.dropout(out) # 保存特征层输出
out = self.fc(features)
return out, features
def feature(self, x):
"""
提取中间层特征向量，用于可视化
返回dropout层的输出（最终层前面的那一层）
"""
with torch.no_grad():
out = self.embedding(x[0])
out = out.unsqueeze(1)
out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
features = self.dropout(out)
return features.cpu().numpy()
def get_prediction(self, x):
"""
获取模型最终层的输出向量（logits）
返回未经softmax的原始输出
"""
with torch.no_grad():
out = self.embedding(x[0])
out = out.unsqueeze(1)
out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
features = self.dropout(out)
predictions = self.fc(features)
return predictions.cpu().numpy()
def prediction(self, features_vector):
"""
根据中间特征向量预测结果
features_vector: 与feature函数输出形状一致的向量
返回预测类别
"""
with torch.no_grad():
if isinstance(features_vector, np.ndarray):
features_vector = torch.tensor(features_vector, dtype=torch.float32).to(self.fc.weight.device)
# 确保输入维度正确
if len(features_vector.shape) == 1:
features_vector = features_vector.unsqueeze(0)
predictions = self.fc(features_vector)
predicted_classes = torch.argmax(predictions, dim=1)
return predicted_classes.cpu().numpy()
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