我在pytorch上找到了一个蒙特卡罗丢弃的实现，实现该方法的主要思想是将模型的丢弃层设置为训练模式。这允许在不同的各种向前传球期间使用不同的漏失掩模。该实现说明了来自各种前向传递的多个预测如何堆叠在一起并用于计算不同的不确定性度量。
import sys
import numpy as np
import torch
import torch.nn as nn
def enable_dropout(model):
""" Function to enable the dropout layers during test-time """
for m in model.modules():
if m.__class__.__name__.startswith('Dropout'):
m.train()
def get_monte_carlo_predictions(data_loader,
forward_passes,
model,
n_classes,
n_samples):
""" Function to get the monte-carlo samples and uncertainty estimates
through multiple forward passes
Parameters
----------
data_loader : object
data loader object from the data loader module
forward_passes : int
number of monte-carlo samples/forward passes
model : object
keras model
n_classes : int
number of classes in the dataset
n_samples : int
number of samples in the test set
"""
dropout_predictions = np.empty((0, n_samples, n_classes))
softmax = nn.Softmax(dim=1)
for i in range(forward_passes):
predictions = np.empty((0, n_classes))
model.eval()
enable_dropout(model)
for i, (image, label) in enumerate(data_loader):
image = image.to(torch.device('cuda'))
with torch.no_grad():
output = model(image)
output = softmax(output) # shape (n_samples, n_classes)
predictions = np.vstack((predictions, output.cpu().numpy()))
dropout_predictions = np.vstack((dropout_predictions,
predictions[np.newaxis, :, :]))
# dropout predictions - shape (forward_passes, n_samples, n_classes)
# Calculating mean across multiple MCD forward passes
mean = np.mean(dropout_predictions, axis=0) # shape (n_samples, n_classes)
# Calculating variance across multiple MCD forward passes
variance = np.var(dropout_predictions, axis=0) # shape (n_samples, n_classes)
epsilon = sys.float_info.min
# Calculating entropy across multiple MCD forward passes
entropy = -np.sum(mean*np.log(mean + epsilon), axis=-1) # shape (n_samples,)
# Calculating mutual information across multiple MCD forward passes
mutual_info = entropy - np.mean(np.sum(-dropout_predictions*np.log(dropout_predictions + epsilon),
axis=-1), axis=0) # shape (n_samples,)
我想做的是计算不同前向传球的准确性，有人能帮助我如何获得准确性，并对实现中使用的尺寸进行任何更改吗
我正在使用CIFAR10数据集，并希望在测试时使用丢弃数据读取器的代码
testset = torchvision.datasets.CIFAR10(root='./data', train=False,download=True, transform=test_transform)
#loading the test set
data_loader = torch.utils.data.DataLoader(testset, batch_size=n_samples, shuffle=False, num_workers=4) ```
准确度是正确分类样本的百分比。您可以创建一个布尔数组，指示某个预测是否等于其相应的参考值，并可以获得这些值的平均值来计算精度。我在下面提供了一个代码示例。
import numpy as np
# 2 forward passes, 4 samples, 3 classes
# shape is (2, 4, 3)
dropout_predictions = np.asarray([
[[0.2, 0.1, 0.7], [0.1, 0.5, 0.4], [0.9, 0.05, 0.05], [0.25, 0.74, 0.01]],
[[0.1, 0.5, 0.4], [0.2, 0.6, 0.2], [0.8, 0.10, 0.10], [0.25, 0.01, 0.74]]
])
# Get the predicted value for each sample in each forward pass.
# Shape of output is (2, 4).
classes = dropout_predictions.argmax(-1)
# array([[2, 1, 0, 1],
# [1, 1, 0, 2]])
# Test equality among the reference values and predicted classes.
# Shape is unchanged.
y_true = np.asarray([2, 1, 0, 1])
elementwise_equal = np.equal(y_true, classes)
# array([[ True, True, True, True],
# [False, True, True, False]])
# Calculate the accuracy for each forward pass.
# Shape is (2,).
elementwise_equal.mean(axis=1)
# array([1. , 0.5])
在上面的例子中，您可以看到第一次前向传球的准确率是100%，第二次前向接球的准确率为50%。
@jakub的答案是正确的。然而，我想提出一种替代方法，这种方法可能会更好，尤其是对更初级的研究人员来说。
Scikit learn具有许多内置的性能测量功能，包括准确性。为了让这些方法与PyTorch一起使用，您只需要将torch张量转换为numpy数组：
x = torch.Tensor(...) # Fill-in as needed
x_np = x.numpy() # Convert to numpy
然后，你只需导入scikit学习：
from sklearn.metrics import accuracy_score
y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
accuracy_score(y_true, y_pred)
这只是返回0.5。容易出错，而且不太可能出错。