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()
