在Keras中获得每个班级的精确度，召回率和F1分数 [英] Getting precision, recall and F1 score per class in Keras

问题描述

我已经使用Keras(2.1.5)中的TensorFlow后端训练了神经网络，并且还使用了keras-contrib(2.0.8)库来添加CRF层作为网络的输出. /p>

我想知道在使用NN对测试集进行预测后如何获得每个班级的精度，召回率和f1分数.

解决方案

假定您具有一个函数get_model()，该函数可以构建与您训练的模型完全相同的模型，并且路径weights_path指向包含模型的HDF5文件重量:

model = get_model()
model.load_weights(weights_path)

这应该可以正确加载模型.然后，您只需定义测试数据的ImageDataGenerator并拟合模型即可获得预测:

# Path to your folder testing data
testing_folder = ""
# Image size (set up the image size used for training)
img_size = 256
# Batch size (you should tune it based on your memory)
batch_size = 16

val_datagen = ImageDataGenerator(
    rescale=1. / 255)
validation_generator = val_datagen.flow_from_directory(
    testing_folder,
    target_size=(img_size, img_size),
    batch_size=batch_size,
    shuffle=False,
    class_mode='categorical')

然后，您可以使用model.predict_generator()方法使模型生成整个数据集的所有预测:

# Number of steps corresponding to an epoch
steps = 100
predictions = model.predict_generator(validation_generator, steps=steps)

最后使用sklearn包中的metrics.confusion_matrix()方法创建混淆矩阵:

val_preds = np.argmax(predictions, axis=-1)
val_trues = validation_generator.classes
cm = metrics.confusion_matrix(val_trues, val_preds)

或使用sklearn中的metrics.precision_recall_fscore_support()方法获得所有类的所有精度，召回率和f1-分数(参数average=None输出所有类的指标):

# label names
labels = validation_generator.class_indices.keys()
precisions, recall, f1_score, _ = metrics.precision_recall_fscore_support(val_trues, val_preds, labels=labels)

我还没有测试过，但是我想这会为您提供帮助.

I have trained a neural network using the TensorFlow backend in Keras (2.1.5) and I have also used the keras-contrib (2.0.8) library in order to add a CRF layer as an output for the network.

I would like to know how can I get the precision, recall and f1 score for each class after making the predictions on a test set using the NN.

解决方案

Assume that you have a function get_model() that builds a your exact same model you have trained and a path weights_path pointing to your HDF5 file containing your model weights:

model = get_model()
model.load_weights(weights_path)

This should load your model properly. Then you just have to define a ImageDataGenerator of your test data and fit the model to obtain predictions:

# Path to your folder testing data
testing_folder = ""
# Image size (set up the image size used for training)
img_size = 256
# Batch size (you should tune it based on your memory)
batch_size = 16

val_datagen = ImageDataGenerator(
    rescale=1. / 255)
validation_generator = val_datagen.flow_from_directory(
    testing_folder,
    target_size=(img_size, img_size),
    batch_size=batch_size,
    shuffle=False,
    class_mode='categorical')

Then you can make the model generate all predictions over your entire dataset using the model.predict_generator() method:

# Number of steps corresponding to an epoch
steps = 100
predictions = model.predict_generator(validation_generator, steps=steps)

And finally create a confussion matrix using the metrics.confusion_matrix() method from sklearn package:

val_preds = np.argmax(predictions, axis=-1)
val_trues = validation_generator.classes
cm = metrics.confusion_matrix(val_trues, val_preds)

Or get all precisions, recalls and f1-scores for all classes using metrics.precision_recall_fscore_support() method from sklearn (argument average=None outputs metrics for all classes):

# label names
labels = validation_generator.class_indices.keys()
precisions, recall, f1_score, _ = metrics.precision_recall_fscore_support(val_trues, val_preds, labels=labels)

I haven't tested it, but I guess this will help you.

这篇关于在Keras中获得每个班级的精确度，召回率和F1分数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！