当tensorflow训练时,如何在网络训练中添加代码,当检测损失值为nan,停止网络训练
时间: 2024-03-02 12:51:22 浏览: 22
在tensorflow训练时,我们可以使用tf.debugging.check_numerics()函数检测损失值是否为NaN或Inf,如果是则停止网络训练。具体实现方法如下:
```python
import tensorflow as tf
# 定义损失函数
loss_function = ...
# 定义优化器
optimizer = ...
# 定义训练步骤
@tf.function
def train_step(inputs, labels):
with tf.GradientTape() as tape:
predictions = model(inputs, training=True)
loss = loss_function(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return loss
# 定义训练过程
def train():
for epoch in range(num_epochs):
for step, (inputs, labels) in enumerate(train_dataset):
loss = train_step(inputs, labels)
# 检查损失值是否为NaN或Inf
if tf.debugging.check_numerics(loss, 'Loss is NaN or Inf!'):
print('Training stopped because loss is NaN or Inf!')
return
print('Epoch: {}, Step: {}, Loss: {}'.format(epoch, step, loss.numpy()))
```
在训练过程中,如果损失值为NaN或Inf,则会输出“Loss is NaN or Inf!”并停止训练。