如何在 PyTorch 中找到混淆矩阵并将其绘制为图像分类器
How to Find Confusion Matrix and Plot it for Image Classifier in PyTorch
基本上这是 VGG-16 模型,我已经执行了迁移学习并对模型进行了微调,我在 2 周前训练了这个模型,发现了测试和训练的准确性,但现在我需要 Class模型的明智准确性也是如此,我正在尝试找出混淆矩阵并想绘制矩阵。培训代码:
# Training the model again from the last CNN Block to The End of the Network
dataset = 'C:\Users\Sara Latif Khan\OneDrive\Desktop\FYP_\Scene15\15-Scene'
model = model.to(device)
optimizer = Adam(filter(lambda p: p.requires_grad, model.parameters()))
#Training Fixed Feature Extractor for 15 epochs
num_epochs = 5
batch_loss = 0
cum_epoch_loss = 0 #cumulative loss for each batch
for e in range(num_epochs):
cum_epoch_loss = 0
for batch, (images, labels) in enumerate(trainloader,1):
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
logps = model(images)
loss = criterion(logps, labels)
loss.backward()
optimizer.step()
batch_loss += loss.item()
print(f'Epoch({e}/{num_epochs} : Batch number({batch}/{len(trainloader)}) : Batch loss : {loss.item()}')
torch.save(model, dataset+'_model_'+str(e)+'.pt')
print(f'Training loss : {batch_loss/len(trainloader)}')
这是我用来根据测试加载程序的数据检查模型准确性的代码。
model. to('cpu')
model.eval()
with torch.no_grad():
num_correct = 0
total = 0
#set_trace ()
for batch, (images,labels) in enumerate(testloader,1):
logps = model(images)
output = torch.exp(logps)
pred = torch.argmax(output,1)
total += labels.size(0)
num_correct += (pred==labels).sum().item()
print(f'Batch ({batch} / {len(testloader)})')
# to check the accuracy of model on 5 batches
# if batch == 5:
# break
print(f'Accuracy of the model on {total} test images: {num_correct * 100 / total }% ')
接下来,我需要找到模型的 class 精度。我正在研究 Jupyter Notebook。我应该重新加载保存的模型并找到 cm 还是什么才是合适的方法。
您必须保存测试集的所有预测和目标。
predictions, targets = [], []
for images, labels in testloader:
logps = model(images)
output = torch.exp(logps)
pred = torch.argmax(output, 1)
# convert to numpy arrays
pred = pred.detach().cpu().numpy()
labels = labels.detach().cpu().numpy()
for i in range(len(pred)):
predictions.append(pred[i])
targets.append(labels[i])
现在您已经存储了 test-set 的所有预测和实际目标。
下一步是创建混淆矩阵。我想我可以给你我经常使用的功能:
def create_confusion_matrix(y_true, y_pred, classes):
""" creates and plots a confusion matrix given two list (targets and predictions)
:param list y_true: list of all targets (in this case integers bc. they are indices)
:param list y_pred: list of all predictions (in this case one-hot encoded)
:param dict classes: a dictionary of the countries with they index representation
"""
amount_classes = len(classes)
confusion_matrix = np.zeros((amount_classes, amount_classes))
for idx in range(len(y_true)):
target = y_true[idx][0]
output = y_pred[idx]
output = list(output).index(max(output))
confusion_matrix[target][output] += 1
fig, ax = plt.subplots(1)
ax.matshow(confusion_matrix)
ax.set_xticks(np.arange(len(list(classes.keys()))))
ax.set_yticks(np.arange(len(list(classes.keys()))))
ax.set_xticklabels(list(classes.keys()))
ax.set_yticklabels(list(classes.keys()))
plt.setp(ax.get_xticklabels(), rotation=45, ha="left", rotation_mode="anchor")
plt.setp(ax.get_yticklabels(), rotation=45, ha="right", rotation_mode="anchor")
plt.show()
所以 y_true 是所有目标,y_pred 所有预测,classes
是将标签映射到实际 class-names 的字典,例如:
classes = {"dog": [1, 0], "cat": [0, 1]}
然后只需调用:
create_confusion_matrix(targets, predictions, classes)
您可能需要稍微调整一下以适应您的代码,但我希望这对您有用。 :)
基本上这是 VGG-16 模型,我已经执行了迁移学习并对模型进行了微调,我在 2 周前训练了这个模型,发现了测试和训练的准确性,但现在我需要 Class模型的明智准确性也是如此,我正在尝试找出混淆矩阵并想绘制矩阵。培训代码:
# Training the model again from the last CNN Block to The End of the Network
dataset = 'C:\Users\Sara Latif Khan\OneDrive\Desktop\FYP_\Scene15\15-Scene'
model = model.to(device)
optimizer = Adam(filter(lambda p: p.requires_grad, model.parameters()))
#Training Fixed Feature Extractor for 15 epochs
num_epochs = 5
batch_loss = 0
cum_epoch_loss = 0 #cumulative loss for each batch
for e in range(num_epochs):
cum_epoch_loss = 0
for batch, (images, labels) in enumerate(trainloader,1):
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
logps = model(images)
loss = criterion(logps, labels)
loss.backward()
optimizer.step()
batch_loss += loss.item()
print(f'Epoch({e}/{num_epochs} : Batch number({batch}/{len(trainloader)}) : Batch loss : {loss.item()}')
torch.save(model, dataset+'_model_'+str(e)+'.pt')
print(f'Training loss : {batch_loss/len(trainloader)}')
这是我用来根据测试加载程序的数据检查模型准确性的代码。
model. to('cpu')
model.eval()
with torch.no_grad():
num_correct = 0
total = 0
#set_trace ()
for batch, (images,labels) in enumerate(testloader,1):
logps = model(images)
output = torch.exp(logps)
pred = torch.argmax(output,1)
total += labels.size(0)
num_correct += (pred==labels).sum().item()
print(f'Batch ({batch} / {len(testloader)})')
# to check the accuracy of model on 5 batches
# if batch == 5:
# break
print(f'Accuracy of the model on {total} test images: {num_correct * 100 / total }% ')
接下来,我需要找到模型的 class 精度。我正在研究 Jupyter Notebook。我应该重新加载保存的模型并找到 cm 还是什么才是合适的方法。
您必须保存测试集的所有预测和目标。
predictions, targets = [], []
for images, labels in testloader:
logps = model(images)
output = torch.exp(logps)
pred = torch.argmax(output, 1)
# convert to numpy arrays
pred = pred.detach().cpu().numpy()
labels = labels.detach().cpu().numpy()
for i in range(len(pred)):
predictions.append(pred[i])
targets.append(labels[i])
现在您已经存储了 test-set 的所有预测和实际目标。 下一步是创建混淆矩阵。我想我可以给你我经常使用的功能:
def create_confusion_matrix(y_true, y_pred, classes):
""" creates and plots a confusion matrix given two list (targets and predictions)
:param list y_true: list of all targets (in this case integers bc. they are indices)
:param list y_pred: list of all predictions (in this case one-hot encoded)
:param dict classes: a dictionary of the countries with they index representation
"""
amount_classes = len(classes)
confusion_matrix = np.zeros((amount_classes, amount_classes))
for idx in range(len(y_true)):
target = y_true[idx][0]
output = y_pred[idx]
output = list(output).index(max(output))
confusion_matrix[target][output] += 1
fig, ax = plt.subplots(1)
ax.matshow(confusion_matrix)
ax.set_xticks(np.arange(len(list(classes.keys()))))
ax.set_yticks(np.arange(len(list(classes.keys()))))
ax.set_xticklabels(list(classes.keys()))
ax.set_yticklabels(list(classes.keys()))
plt.setp(ax.get_xticklabels(), rotation=45, ha="left", rotation_mode="anchor")
plt.setp(ax.get_yticklabels(), rotation=45, ha="right", rotation_mode="anchor")
plt.show()
所以 y_true 是所有目标,y_pred 所有预测,classes
是将标签映射到实际 class-names 的字典,例如:
classes = {"dog": [1, 0], "cat": [0, 1]}
然后只需调用:
create_confusion_matrix(targets, predictions, classes)
您可能需要稍微调整一下以适应您的代码,但我希望这对您有用。 :)