基于TensorBoard的模型训练过程监控

基于TensorBoard的模型训练过程监控

监控指标配置

在TensorBoard中配置关键训练指标监控：

import tensorflow as tf
from datetime import datetime

# 创建日志目录
log_dir = "logs/fit/mnist/" + datetime.now().strftime("%Y%m%d-%H%M%S")
writer = tf.summary.create_file_writer(log_dir)

# 监控损失函数和准确率
@tf.function
def train_step(x, y):
    with tf.GradientTape() as tape:
        predictions = model(x, training=True)
        loss = loss_function(y, predictions)
        
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    
    # 记录指标
    tf.summary.scalar('loss', loss, step=optimizer.iterations)
    tf.summary.scalar('accuracy', accuracy_metric(y, predictions), step=optimizer.iterations)
    
    return loss

告警配置方案

配置TensorBoard的实时监控告警：

1. 损失值异常检测：当训练损失连续5个step增长超过0.1时触发告警
2. 准确率下降检测：当验证准确率连续3个epoch下降超过0.02时触发告警

# 告警监控逻辑
class TrainingMonitor:
    def __init__(self, threshold_loss=0.1, threshold_acc=0.02):
        self.loss_history = deque(maxlen=5)
        self.acc_history = deque(maxlen=3)
        
    def check_anomaly(self, current_loss, current_acc):
        # 检查损失异常
        if len(self.loss_history) == 4:
            avg_increase = (sum(self.loss_history) - self.loss_history[0]) / 4
            if avg_increase > threshold_loss:
                self.send_alert("Loss anomaly detected")
        
        # 检查准确率异常
        if len(self.acc_history) == 2:
            if self.acc_history[0] - self.acc_history[-1] > threshold_acc:
                self.send_alert("Accuracy drop detected")

部署建议

• 启动TensorBoard服务：tensorboard --logdir=logs --port=6006
• 配置Prometheus抓取指标：通过TensorFlow的tf.summary导出到Prometheus格式
• 设置Webhook告警：当监控指标超出阈值时触发Slack/钉钉通知