The purpose of loss functions is to compute the quantity that a model should seek to minimize during training.
https://tensorflow.google.cn/api_docs/python/tf/keras/metrics
A metric is a function that is used to judge the performance of your model.
Metric functions are similar to loss functions, except that the results from evaluating a metric are not used when training the model. Note that you may use any loss function as a metric.
loss与metrics对比理解
loss常用方法
参考自：https://keras.io/zh/losses/
更多细节参考：https://keras.io/api/losses/
连续型：
mean_squared_error 或 mse
mean_absolute_error 或 mae
mean_absolute_percentage_error 或 mape
mean_squared_logarithmic_error 或 msle
squared_hinge
hinge
categorical_hinge
logcosh 预测误差的双曲余弦的对数。
类别型：
categorical_crossentropy： 亦称作多类的对数损失，注意使用该目标函数时，需要将标签转化为形如(nb_samples, nb_classes)的二值序列
sparse_categorical_crossentropy
binary_crossentropy （亦称作对数损失，logloss）
kullback_leibler_divergence
poisson
cosine_proximity 即预测值与真实标签的余弦距离平均值的相反数
keras.Sequential.compile(loss='目标函数 ', optimizer=‘adam’, metrics=[‘accuracy’])
metrics常用方法
中文：https://keras.io/zh/metrics/
英文：https://keras.io/api/metrics/
binary_accuracy
categorical_accuracy
sparse_categorical_accuracy
top_k_categorical_accuracy
sparse_top_k_categorical_accuracy
自定义评价函数
使用方法
compile方法中的使用
# 方法1
model.compile(loss='mean_squared_error',
optimizer='sgd',
metrics=['mae', 'acc'])
# 方法2
from keras import metrics
model.compile(loss='mean_squared_error',
optimizer='sgd',
metrics=[metrics.mae, metrics.categorical_accuracy])
5、参考资料
官网资料
英文：https://keras.io/api/losses/
英文：https://keras.io/api/metrics/
中文：https://keras.io/zh/losses/
中文：https://keras.io/zh/metrics/
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https://tensorflow.google.cn/api_docs/python/tf/keras/metrics/CategoricalCrossentropy
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