def __init__(self): print('Configuring CNN model...') if not os.path.exists(vocab_dir): # 如果不存在词汇表,重建 build_vocab(train_dir, vocab_dir, config.vocab_size) self.categories, cat_to_id = read_category() words, self.word_to_id = read_vocab(vocab_dir) self.table = pd.read_excel('predict_check_data.xls') category_set = list(set(self.table['name'].tolist())) self.config = TCNNConfig(len(list(category_set))) self.config.vocab_size = len(words) self.model = TextCNN(self.config) self.categories = list(set(self.table['name'].tolist())) self.categories.sort(key=self.table['name'].tolist().index)
print(y_pred_cls) cm = metrics.confusion_matrix(y_test_cls, y_pred_cls) print(cm) time_dif = get_time_dif(start_time) print("Time usage:", time_dif) if __name__ == '__main__': # if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']: # raise ValueError("""usage: python run_cnn.py [train / test]""") print('Configuring CNN model...') config = TCNNConfig() if not os.path.exists(vocab_dir): # 如果不存在词汇表,重建 build_vocab(train_dir, vocab_dir, config.vocab_size) categories, cat_to_id = read_category() words, word_to_id = read_vocab(vocab_dir) config.vocab_size = len(words) dataNums = [16, 32, 64, 128, 256] for i in dataNums: if i == 0: continue g1 = tf.Graph() sess1 = tf.Session(graph=g1) with sess1.as_default(): with g1.as_default(): model = TextCNN(config, batch_size=i) train() test() plt.plot(xx, yy1)
config = TRNNConfig() t_name = sys.argv[3] t_th = sys.argv[2] data_dir = sys.argv[4] base_dir = 'data/' + data_dir + '/' + t_name classes = sys.argv[5].split('-') train_dir = os.path.join(base_dir, 'train.csv') test_dir = os.path.join(base_dir, 'test.csv') val_dir = os.path.join(base_dir, 'dev.csv') vocab_dir = os.path.join('data/data_orginal/'+t_name, 'vocab.csv') if not os.path.exists(vocab_dir): # 如果不存在词汇表,重建 print(' vocab_dir not exists: ',vocab_dir) build_vocab('data/data_orginal/'+t_name+'/whole.csv', vocab_dir, config.vocab_size) categories, cat_to_id = read_category(classes) words, word_to_id = read_vocab(vocab_dir) config.vocab_size = len(words) config.num_classes = len(classes) mode_name = 'textrnn' save_dir = 'checkpoints/' + t_name + '/' + mode_name + '_' + t_name + "_" + data_dir + '_' + t_th + 'th' print('save_dir:', save_dir) save_path = os.path.join(save_dir, 'best_validation') # 最佳验证结果保存路径 model = TextRNN(config) if sys.argv[1] == 'train': train() else: test()
msg = 'Iter: {0:>6}, Train Loss: {1:>6.2}, Train Acc: {2:>7.2%},' \ + ' Val Loss: {3:>6.2}, Val Acc: {4:>7.2%}, Time: {5} {6}' print( msg.format(total_batch, loss_train, acc_train, loss_val, acc_val, time_dif, improved_str)) session.run(train_op, feed_dict=feed_dict) # 运行优化 total_batch += 1 if total_batch - last_improved > require_improvement: # 验证集正确率长期不提升,提前结束训练 print("No optimization for a long time, auto-stopping...") flag = True break # 跳出循环 if flag: # 同上 break if __name__ == '__main__': model = TextCNN() config = model.config file_config, _ = Config().parse.parse_known_args() print('Configuring CNN model...') if not os.path.exists(file_config.vocab_path): # 如果不存在词汇表,重建 build_vocab(file_config.train_path, file_config.vocab_path, config.vocab_size) categories, cat_to_id = read_category() words, word_to_id = read_vocab(file_config.vocab_path) config.vocab_size = len(words) train(model, file_config)
print("Confusion Matrix...") cm = metrics.confusion_matrix(y_test_cls, y_pred_cls) print(cm) time_dif = get_time_dif(start_time) print("Time usage:", time_dif) if __name__ == '__main__': # if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']: # raise ValueError("""usage: python run_cnn.py [train / test]""") choice = input("train or test:") # if choice=='train': # create_file(data_dir,train_dir,test_dir,val_dir,4000,1000) print('Configuring CNN model...') config = TCNNConfig() if not os.path.exists(vocab_dir): # 如果不存在词汇表,重建 build_vocab(train_dir, vocab_dir, 100000) categories, cat_to_id = read_category() words, word_to_id = read_vocab(vocab_dir) config.vocab_size = len(words) filter_sizes = [3, 4, 5] # 3 num_filters = 32 #model = CNN(config.seq_length,config.num_classes,config.vocab_size,config.embedding_dim,filter_sizes,num_filters,0.0) model = TextCNN(config) if choice == 'train': train() else: test()
# 评估 print("Precision, Recall and F1-Score...") print(metrics.classification_report(y_test_cls, y_pred_cls, target_names=categories)) # 混淆矩阵 print("Confusion Matrix...") cm = metrics.confusion_matrix(y_test_cls, y_pred_cls) print(cm) time_dif = get_time_dif(start_time) print("Time usage:", time_dif) if __name__ == '__main__': if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']: raise ValueError("""usage: python run_rnn.py [train / test]""") print('Configuring RNN model...') config = TRNNConfig() if not os.path.exists(vocab_dir): # 如果不存在词汇表,重建 build_vocab(train_dir, vocab_dir, config.vocab_size) categories, cat_to_id = read_category() words, word_to_id = read_vocab(vocab_dir) config.vocab_size = len(words) model = TextRNN(config) if sys.argv[1] == 'train': train() else: test()
metrics.classification_report(y_test_cls, y_pred_cls, target_names=categories)) # 混淆矩阵 print("Confusion Matrix...") cm = metrics.confusion_matrix(y_test_cls, y_pred_cls) print(cm) time_dif = get_time_dif(start_time) print("Time usage:", time_dif) if __name__ == '__main__': if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']: raise ValueError("""usage: python run_rnn.py [train / test]""") print('Configuring RNN model...') config = TRNNConfig() if not os.path.exists(vocab_path): # 如果不存在词汇表,重建 build_vocab(train_path, vocab_path, config.vocab_size) categories, cat_to_id = read_category() words, word_to_id = read_vocab(vocab_path) config.vocab_size = len(words) model = TextRNN(config) if sys.argv[1] == 'train': train() else: test()
val_dir = os.path.join(base_dir, 'cnewsval.txt') vocab_dir = os.path.join(base_dir, 'cnewsvocab.txt') vector_word_dir= os.path.join(base_dir, 'vector_word.txt')#vector_word trained by word2vec vector_word_npz=os.path.join(base_dir, 'vector_word.npz')# save vector_word to numpy file #最佳验证结果保存路径 save_dir = r'HOME\mydata\lstm\checkpoints' save_path = os.path.join(save_dir, 'best_validation') #获取词典 '''build_vocab(train_dir,vocab_dir) _,word_to_id=read_vocab(vocab_dir) categories,cat_to_id=read_category() config=TRNNConfig() model=TextRNN(config)''' config=TRNNConfig() build_vocab(train_dir,vocab_dir) words,word_to_id=read_vocab(vocab_dir) categories,cat_to_id=read_category() config.vocab_size = len(words) if not os.path.exists(vector_word_npz): export_word2vec_vectors(word_to_id, vector_word_dir, vector_word_npz) config.pre_trianing = get_training_word2vec_vectors(vector_word_npz) model=TextRNN(config) init=tf.global_variables_initializer() def get_time_dif(start_time): """获取已使用时间""" end_time = time.time() time_dif = end_time - start_time return timedelta(seconds=int(round(time_dif)))
def loaddata(): from sklearn.datasets import load_files from sklearn.model_selection import train_test_split dir = 'C:\\Users\\chenshuai\\Desktop\\py\\dataset\\individual\\' paper = load_files(dir, encoding='UTF-8') x_train, x_test, y_train, y_test = train_test_split(paper.data, paper.target, test_size=0.2) return x_train, y_train, x_test, y_test if __name__ == '__main__': # if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']: # raise ValueError("""usage: python run_cnn.py [train / test]""") print('Configuring CNN model...') config = TCNNConfig() print('Loading raw data...') x_train, y_train, x_test, y_test = loaddata() if not os.path.exists(vocab_dir): # 如果不存在词汇表,重建 print('Building vocab...') build_vocab(x_train, vocab_dir, config.vocab_size) categories, cat_to_id = read_category() words, word_to_id = read_vocab(vocab_dir) config.vocab_size = len(words) model = TextCNN(config) # if sys.argv[1] == 'train': train(x_train, y_train) # else: test(x_test, y_test)
def train(model,data): if print("Configuring TensorBoard and Saver...") # 配置 Tensorboard,重新训练时,请将tensorboard文件夹删除,不然图会覆盖 tensorboard_dir = 'tensorboard/textcnn' if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) tf.summary.scalar("loss", model.loss) tf.summary.scalar("accuracy", model.acc) merged_summary = tf.summary.merge_all() writer = tf.summary.FileWriter(tensorboard_dir) # 配置 Saver saver = tf.train.Saver() if not os.path.exists(save_dir): os.makedirs(save_dir) print("Loading training and validation data...") # 载入训练集与验证集 start_time = time.time() x_train, y_train = process_file(train_dir, word_to_id, cat_to_id, config.seq_length) x_val, y_val = process_file(val_dir, word_to_id, cat_to_id, config.seq_length) time_dif = get_time_dif(start_time) print("Time usage:", time_dif) # 创建session session = tf.Session() session.run(tf.global_variables_initializer()) writer.add_graph(session.graph) print('Training and evaluating...') start_time = time.time() total_batch = 0 # 总批次 best_acc_val = 0.0 # 最佳验证集准确率 last_improved = 0 # 记录上一次提升批次 require_improvement = 1000 # 如果超过1000轮未提升,提前结束训练 flag = False for epoch in range(config.num_epochs): print('Epoch:', epoch + 1) batch_train = batch_iter(x_train, y_train, config.batch_size) for x_batch, y_batch in batch_train: feed_dict = feed_data(x_batch, y_batch, config.dropout_keep_prob) if total_batch % config.save_per_batch == 0: # 每多少轮次将训练结果写入tensorboard scalar s = session.run(merged_summary, feed_dict=feed_dict) writer.add_summary(s, total_batch) if total_batch % config.print_per_batch == 0: # 每多少轮次输出在训练集和验证集上的性能 feed_dict[model.keep_prob] = 1.0 loss_train, acc_train = session.run([model.loss, model.acc], feed_dict=feed_dict) loss_val, acc_val = evaluate(session, x_val, y_val) # todo if acc_val > best_acc_val: # 保存最好结果 best_acc_val = acc_val last_improved = total_batch saver.save(sess=session, save_path=save_path) improved_str = '*' else: improved_str = '' time_dif = get_time_dif(start_time) msg = 'Iter: {0:>6}, Train Loss: {1:>6.2}, Train Acc: {2:>7.2%},' \ + ' Val Loss: {3:>6.2}, Val Acc: {4:>7.2%}, Time: {5} {6}' print(msg.format(total_batch, loss_train, acc_train, loss_val, acc_val, time_dif, improved_str)) session.run(model.optim, feed_dict=feed_dict) # 运行优化 total_batch += 1 if total_batch - last_improved > require_improvement: # 验证集正确率长期不提升,提前结束训练 print("No optimization for a long time, auto-stopping...") flag = True break # 跳出循环 if flag: # 同上 break def test(): print("Loading test data...") start_time = time.time() x_test, y_test = process_file(test_dir, word_to_id, cat_to_id, config.seq_length) session = tf.Session() session.run(tf.global_variables_initializer()) saver = tf.train.Saver() saver.restore(sess=session, save_path=save_path) # 读取保存的模型 print('Testing...') loss_test, acc_test = evaluate(session, x_test, y_test) msg = 'Test Loss: {0:>6.2}, Test Acc: {1:>7.2%}' print(msg.format(loss_test, acc_test)) batch_size = 128 data_len = len(x_test) num_batch = int((data_len - 1) / batch_size) + 1 y_test_cls = np.argmax(y_test, 1) y_pred_cls = np.zeros(shape=len(x_test), dtype=np.int32) # 保存预测结果 for i in range(num_batch): # 逐批次处理 start_id = i * batch_size end_id = min((i + 1) * batch_size, data_len) feed_dict = { model.input_x: x_test[start_id:end_id], model.keep_prob: 1.0 } y_pred_cls[start_id:end_id] = session.run(model.y_pred_cls, feed_dict=feed_dict) # 评估 print("Precision, Recall and F1-Score...") print(metrics.classification_report(y_test_cls, y_pred_cls, target_names=categories)) # 混淆矩阵 print("Confusion Matrix...") cm = metrics.confusion_matrix(y_test_cls, y_pred_cls) print(cm) time_dif = get_time_dif(start_time) print("Time usage:", time_dif) if __name__ == '__main__': #if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']: # raise ValueError("""usage: python run_cnn.py [train / test]""") print('Configuring CNN model...') config = TCNNConfig() if not os.path.exists(vocab_dir): # 如果不存在词汇表,重建 build_vocab(train_dir, vocab_dir, config.vocab_size) categories, cat_to_id = read_category() words, word_to_id = read_vocab(vocab_dir) config.vocab_size = len(words) model = TextCNN(config) #if sys.argv[1] == 'train': # train() #else: # test() train()