def get_the_final_result():
# 参数配置
batch_size = 512
seq_length = 20
embeddings_size = 300
hidden_size = 256
num_layers = 2
num_classes = 9
learning_rate = 0.003
dropout = 0.3
# 数据文件路径
word2vec_path = './data/word2vec.bin'
train_file = './data/train.json'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 定义模型
model = TextRCNN(embeddings_size, num_classes, hidden_size, num_layers,
True, dropout)
model.to(device)
# 加载训练好的模型参数
checkpoints = torch.load('./saved_model/text_rcnn.pth')
model.load_state_dict(checkpoints['model_state'])
# 加载数据
data_loader = Dataloader(word2vec_path, batch_size, embeddings_size,
seq_length, device) # 初始化数据迭代器
texts, labels = data_loader.load_data(train_file,
shuffle=True,
mode='train') # 加载数据
print('Data load completed...')
# 在测试集上进行测试
test_texts = texts[int(len(texts) * 0.8):]
test_labels = labels[int(len(texts) * 0.8):]
steps = len(test_texts) // batch_size
loader = data_loader.data_iterator(test_texts, test_labels)
# 测试集上的准确率
accuracy = evaluate(model, loader, steps)
print('The final result(Accuracy in Test) is %.2f' % (accuracy * 100))
if __name__ == "__main__":
# 参数配置
epochs = 50
batch_size = 512
seq_length = 20
embeddings_size = 300
hidden_size = 256
num_layers = 2
num_classes = 9
learning_rate = 0.003
dropout = 0.3
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 设置随机种子
random.seed(2020)
torch.manual_seed(2020)
# 加载文本分类模型 TextRCNN
model = TextRCNN(embeddings_size, num_classes, hidden_size, num_layers, True, dropout)
model.to(device)
# 定义损失函数和优化器
criterian = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=learning_rate)
scheduler = LambdaLR(optimizer, lr_lambda=lambda epoch: 1/(1 + 0.05 * epoch))
print('-' * 100)
train_and_test(model, optimizer, criterian, scheduler, batch_size, embeddings_size, seq_length, './saved_model/text_rcnn.pth', epochs, device)