def evaluate_accuracy(net, data_iter): # evaluate 估计 accuracy 精确性 if isinstance(net, torch.nn.Module): # instance 例子 net.eval() # evalution metric = Accumulator(2) # metric 公制 度量标准 accumulate 累加 with torch.no_grad(): for X, y in data_iter: metric.add(accuracy(net(X), y), y.numel()) # numel number of element return metric[0] / metric[1] # metric 度量标准 报错 ZeroDivisionError: float division by zero
时间: 2023-12-03 10:46:04 浏览: 40
这个错误是因为 `metric[1]` 的值为 0,导致除数为 0,无法进行除法操作。可能是因为 `data_iter` 中没有数据,或者数据的标签 `y` 所含的元素个数为 0。
你可以在调用 `evaluate_accuracy` 函数之前打印一下 `data_iter` 中的数据,检查一下是否为空。如果不为空,可以在函数中加入一个判断语句,避免除数为 0 的情况发生。例如:
```python
def evaluate_accuracy(net, data_iter):
if isinstance(net, torch.nn.Module):
net.eval()
metric = Accumulator(2)
with torch.no_grad():
for X, y in data_iter:
metric.add(accuracy(net(X), y), y.numel())
if metric[1] == 0:
return 0.0
else:
return metric[0] / metric[1]
```
这样就可以避免 ZeroDivisionError 的问题了。
相关问题
def evaluate_accuracy(net, data_iter):
This function computes the accuracy of a neural network model on a given dataset. Here is a possible implementation:
```
def evaluate_accuracy(net, data_iter):
acc_sum, n = 0.0, 0
for X, y in data_iter:
y_hat = net(X)
acc_sum += (y_hat.argmax(axis=1) == y).sum().item()
n += y.size()[0]
return acc_sum / n
```
The function takes two arguments:
- `net`: the neural network model to evaluate.
- `data_iter`: an iterator that generates batches of input data and labels.
The function iterates over the batches in `data_iter`, computes the predicted labels for each batch using the `net` model, and compares these predictions to the true labels to compute the number of correct predictions. It then returns the accuracy, which is the fraction of correct predictions over the total number of examples.
Note that `net(X)` assumes that the model has a `forward` method that takes input data `X` and returns the predicted outputs. Also, `y_hat.argmax(axis=1)` computes the predicted label for each example in the batch by selecting the index with the highest score along the second axis (i.e., the class dimension), and `(y_hat.argmax(axis=1) == y).sum().item()` computes the number of correct predictions in the batch. Finally, `y.size()[0]` computes the number of examples in the batch.
def evaluate_accuracy(net, data_iter): #@save """计算在指定数据集上模型的精度""" if isinstance(net, torch.nn.Module): net.eval() # 将模型设置为评估模式 metric = Accumulator(2) # 正确预测数、预测总数 with torch.no_grad(): for X, y in data_iter:
这段代码实现了一个计算模型在指定数据集上精度的函数。具体来说,它接受一个神经网络模型 `net` 和一个数据迭代器 `data_iter`,并返回模型在数据集上的正确预测数和预测总数。
首先,`isinstance(net, torch.nn.Module)` 判断 `net` 是否是 `torch.nn.Module` 类型,如果是则将模型设置为评估模式。`metric = Accumulator(2)` 创建一个累加器对象,用于累加正确预测数和预测总数。
接下来,我们使用 `torch.no_grad()` 上下文管理器来关闭自动求导,以减少内存消耗。然后使用 `data_iter` 迭代数据集中的每个样本,对于每个样本,我们调用模型 `net` 对其进行预测,将预测结果与真实标签 `y` 进行比较,如果预测正确,则将正确预测数加 1。最后,返回正确预测数和预测总数。