我的CNN模型运行后,我一直试图运行一个混淆矩阵。
我的模型是对狗/兔子进行分类。
以下是我所做的:
我将每个班级(狗/婴儿)的照片放在两个文件夹中的单独文件夹中:培训和测试。
培训目录->兔子目录->兔子图像
训练目录->小狗目录->小狗图像
测试目录->兔子目录->兔子图像
测试目录->小狗目录->小狗图像
我使用以下代码从文件夹中获取图像:
training_data = train_datagen.flow_from_directory('./images/train',
target_size = (28, 28),
batch_size = 86,
class_mode = 'binary',
color_mode='rgb',
classes=None)
test_data = test_datagen.flow_from_directory('./images/test',
target_size = (28, 28),
batch_size = 86,
class_mode = 'binary',
color_mode='rgb',
classes=None)
我使用以下代码将图像分离为训练/验证。
data_generator = ImageDataGenerator(
validation_split=0.2,
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
)
train_generator = data_generator.flow_from_directory(
'./images/train',
target_size = (28, 28),
batch_size = 86,
class_mode = 'binary',
color_mode='rgb',
classes=None, subset="training"
)
validation_generator = data_generator.flow_from_directory(
'./images/train',
target_size = (28, 28),
batch_size = 86,
class_mode = 'binary',
color_mode='rgb',
classes=None, subset="validation"
)
history=classifier.fit_generator(
train_generator,
steps_per_epoch = (8000 / 86),
epochs = 2,
validation_data = validation_generator,
validation_steps = 8000/86,
callbacks=[learning_rate_reduction]
)
当我尝试运行
confusion_matrix(validation_data)
时,出现以下错误:TypeError: confusion_matrix() missing 1 required positional argument: 'y_pred'
当我跑步时
#Confusion matrix
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
# Predict the values from the validation dataset
Y_pred = classifier.predict(training_data)
# Convert predictions classes to one hot vectors
Y_pred_classes = np.argmax(Y_pred,axis = 1)
# Convert validation observations to one hot vectors
Y_true = np.argmax(training_data,axis = 1)
# compute the confusion matrix
confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)
# plot the confusion matrix
plot_confusion_matrix(confusion_mtx, classes = range(10))
sns.heatmap(confusion_mtx, annot=True, fmt='d')
我收到以下错误
AttributeError: 'DirectoryIterator' object has no attribute 'ndim'
最佳答案
据我了解,您想使用混淆矩阵和热图来验证分类器模型。
我还对垃圾邮件文本分类进行了验证,因此您可以执行此操作,
对于混淆矩阵,
from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, y_pred)
print(conf_mat)
对于热图,
import seaborn as sns
conf_mat = confusion_matrix(y_test, y_pred)
conf_mat_normalized = conf_mat.astype('float') / conf_mat.sum(axis=1)[:, np.newaxis]
sns.heatmap(conf_mat_normalized)
plt.ylabel('True label')
plt.xlabel('Predicted label')
仅仅意味着混淆矩阵需要两个参数(您的实际真相标签和预测的标签列表)
希望对您有帮助。
关于python-3.x - CNN-尝试使用seaborn.heatmap运行混淆矩阵,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/54269849/