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:
时间: 2023-07-14 19:13:03 浏览: 68
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。
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.
为代码添加注释def evaluate_accuracy_gpu(net, data_iter, device=None): #@save """使⽤GPU计算模型在数据集上的精度""" if isinstance(net, nn.Module): net.eval() # 设置为评估模式 if not device: device = next(iter(net.parameters())).device # 正确预测的数量,总预测的数量 metric = d2l.Accumulator(2) with torch.no_grad(): for X, y in data_iter: if isinstance(X, list): # BERT微调所需的(之后将介绍) X = [x.to(device) for x in X] else: X = X.to(device) y = y.to(device) metric.add(d2l.accuracy(net(X), y), y.numel()) return metric[0] / metric[1]
# 该函数用于在GPU上计算模型在数据集上的准确度,并返回准确度
def evaluate_accuracy_gpu(net, data_iter, device=None):
# 如果net是nn.Module实例,则将其设置为评估模式
if isinstance(net, nn.Module):
net.eval()
# 如果没有指定device,则使用第一个参数的设备
if not device:
device = next(iter(net.parameters())).device
# metric用于记录正确预测的数量和总预测的数量
metric = d2l.Accumulator(2)
with torch.no_grad():
# 遍历数据集中的每个样本,并在GPU上计算模型的输出
for X, y in data_iter:
# 如果X是一个列表,则表示需要对BERT微调进行处理
if isinstance(X, list):
X = [x.to(device) for x in X]
else:
X = X.to(device)
y = y.to(device)
# 计算模型在数据集上的准确度,并将结果添加到metric中
metric.add(d2l.accuracy(net(X), y), y.numel())
# 返回准确度
return metric[0] / metric[1]