我有一个包含 15 个不平衡类的数据集,并尝试使用 keras 进行多标签分类。
我正在尝试使用微型 F-1 分数作为指标。
我的模型:
# Create a VGG instance
model_vgg = tf.keras.applications.VGG19(weights = 'imagenet', pooling = 'max', include_top = False,
input_shape = (512, 512, 3))
# Freeze the layers which you don't want to train.
for layer in model_vgg.layers[:-5]:
layer.trainable = False
# Adding custom Layers
x = model_vgg.output
x = Flatten()(x)
x = Dense(1024, activation = "relu")(x)
x = Dropout(0.5)(x)
x = Dense(1024, activation = "relu")(x)
predictions = Dense(15, activation = "sigmoid")(x)
# creating the final model
model_vgg_final = Model(model_vgg.input, predictions)
# Print the summary
model_vgg_final.summary()
对于 F1 分数,我使用来自 this question 的自定义指标
from keras import backend as K
def f1(y_true, y_pred):
def recall(y_true, y_pred):
"""Recall metric.
Only computes a batch-wise average of recall.
Computes the recall, a metric for multi-label classification of
how many relevant items are selected.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
"""Precision metric.
Only computes a batch-wise average of precision.
Computes the precision, a metric for multi-label classification of
how many selected items are relevant.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
precision = precision(y_true, y_pred)
recall = recall(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
我在编译模型时使用二元交叉熵和自定义 F-1
# Compile a model
model_vgg_final.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = [f1])
我监控 F-1 是否提前停止
# Early stopping
early_stopping = EarlyStopping(monitor = 'f1', patience = 5)
# Training the model
history_vgg = model_vgg_final.fit(train_generator, steps_per_epoch = 10, epochs = 30, verbose = 1,
callbacks = [early_stopping], validation_data = valid_generator)
如何更新此自定义函数并获取 micro F-1 作为指标?也感谢有关我的方法的提示。
scikit-learn documentation中有信息, 但不确定如何将其合并到 keras 中
最佳答案
好问题。
您在那里提供的链接指向如何在旧版本的 Keras 中计算指标(请耐心等待,简短的解释)。问题是,在较旧的 Keras (1.X) 中,指标是按批处理计算的,这当然会导致不正确的全局结果。在 Keras 2.X 中,内置指标已被删除。
但是,您的问题有解决方案。
- 您可以实现自己的自定义回调。你可以在这里查看我的答案,它保证在 TensorFlow
2.x
中工作:How to get other metrics in Tensorflow 2.0 (not only accuracy)? - 您可以使用
tensorflow-addons
-->pip install tensorflow-addons
。 TensorFlow addons是一个非常好的包,它包含多种功能和特性,这些功能和特性在基本 TensorFlow 包中是不可用的。此处,F1Score
是一个内置指标,因此您可以直接使用它。
例子:
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.00001),
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=[tf.keras.metrics.BinaryAccuracy(),
tfa.metrics.F1Score(num_classes=number_of_classes,
average='micro',
threshold=0.5)])
请注意“micro
”参数的用法,它实际上代表了您想要的,micro f1-score
。
关于python - 在keras中计算微F-1分数,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/66554207/