请帮我写出模型训练准确度的代码
时间: 2024-05-01 10:22:58 浏览: 10
模型训练准确度的计算方式和代码实现会因模型类型和任务类型而有所不同。这里提供两个示例代码:
1. 分类任务的模型训练准确度计算代码(假设使用PyTorch框架):
```python
def train(model, train_loader, optimizer, criterion):
model.train()
total_loss = 0
correct = 0
total = 0
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
total_loss += loss.item()
_, predicted = output.max(1)
total += target.size(0)
correct += predicted.eq(target).sum().item()
train_loss = total_loss / len(train_loader.dataset)
train_acc = 100. * correct / len(train_loader.dataset)
return train_loss, train_acc
```
2. 生成任务的模型训练准确度计算代码(假设使用TensorFlow框架):
```python
def train(model, dataset, optimizer, loss_fn):
total_loss = 0
total_tokens = 0
total_correct = 0
for input_seq, target_seq in dataset:
with tf.GradientTape() as tape:
output = model(input_seq)
loss = loss_fn(target_seq, output)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
total_loss += loss.numpy()
total_tokens += tf.reduce_sum(target_seq != 0)
total_correct += tf.reduce_sum(tf.cast(tf.math.equal(tf.math.argmax(output, axis=-1), target_seq), tf.float32))
train_loss = total_loss / total_tokens
train_acc = total_correct / total_tokens
return train_loss, train_acc
```
这些代码仅供参考,具体实现要根据模型和任务类型进行调整。