def evaluate(sess, x1_, x2_, y_):
"""评估在某一数据上的准确率和损失"""
data_len = len(x1_)
batch_eval = batch_iter(x1_, x2_, y_, 128)
total_loss = 0.0
total_acc = 0.0
for x1_batch, x2_batch, y_batch in batch_eval:
batch_len = len(x1_batch)
feed_dict = feed_data(x1_batch, x2_batch, y_batch, 1.0)
loss, acc = sess.run([model.loss, model.acc], feed_dict=feed_dict)
total_loss += loss * batch_len
total_acc += acc * batch_len
return total_loss / data_len, total_acc / data_len
def train():
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
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
# 结果可视化与存储
tf.summary.scalar("loss", model.loss) # 可视化loss
tf.summary.scalar("accuracy", model.acc) # 可视化acc
merged_summary = tf.summary.merge_all() # 将所有操作合并输出
writer = tf.summary.FileWriter(tensorboard_dir) # 将summary data写入磁盘
# 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()
train_1, train_2, train_output = p.setinputdata(model.config.seq_length_1,
model.config.seq_length_2,
flag=0)
# print(train_3)
val_1, val_2, val_output = p.setinputdata(model.config.seq_length_1,
model.config.seq_length_2,
flag=1)
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(train_1, train_2, train_output,
config.batch_size)
for x1_batch, x2_batch, y_batch in batch_train:
feed_dict = feed_data(x1_batch, x2_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, val_1, val_2,
val_output) # 验证当前会话中的模型的loss和acc
if acc_val > best_acc_val:
# 保存最好结果
best_acc_val = acc_val
last_improved = total_batch
print('---在这里做了修改---保存所有提升的---')
saver.save(sess=session,
save_path=save_path + "_" + str(total_batch))
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}'
fmsg = msg.format(total_batch, loss_train, acc_train, loss_val,
acc_val, time_dif, improved_str)
print(fmsg)
# 害怕控制台的信息看不到，再保存到log文件里
filename = '../result/printLog_attention_lstm_2.txt'
with open(filename, 'a+') as f:
f.write(fmsg + '\n')
# 害怕控制台的信息看不到，再保存到log文件里
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