TensorFlow损失函数
损失函数又称成本函数或目标函数，是真实值与预测值之间的差异，模型优化目标是最小化这个差异值。
from tensorflow import keras
from tensorflow.keras import layers
model = keras.Sequential()
model.add(layers.Dense(64, kernel_initializer='uniform', input_shape=(10,)))
model.add(layers.Activation('softmax'))
loss_function = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(loss=loss_function, optimizer='adam')
model.compile(loss='sparse_categorical_crossentropy', optimizer='Adam')
### 1. 二元交叉熵
# 输出数据为0-1之间，模型输出层激活函数为sigmoid
import tensorflow as tf
y_true = [[0., 1.], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6]]
y_pred = [[0.6, 0.4], [0.4, 0.6], [0.6, 0.4], [0.8, 0.2]]
bce = tf.keras.losses.BinaryCrossentropy(reduction='sum_over_batch_size')
print(bce(y_true, y_pred).numpy()) # 0.839445
bce = tf.keras.losses.BinaryCrossentropy(reduction='sum')
print(bce(y_true, y_pred).numpy()) # 3.35778
bce = tf.keras.losses.BinaryCrossentropy(reduction='none')
print(bce(y_true, y_pred).numpy()) # [0.9162905 0.5919184 0.79465103 1.0549198 ]
### 2.分类交叉熵CategoricalCrossentropy
# 样本标签为one-hot编码,激活函数为softmax
y_true = [[0, 1, 0], [0, 0, 1]]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
cce = tf.keras.losses.CategoricalCrossentropy()
print(cce(y_true, y_pred).numpy())
print(cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy())
cce = tf.keras.losses.CategoricalCrossentropy(reduction='sum')
print(cce(y_true, y_pred).numpy())
cce = tf.keras.losses.CategoricalCrossentropy(reduction='none')
print(cce(y_true, y_pred).numpy())
### 3.分类交叉熵SparseCategoricalCrossentropy
# 样本标签为索引位置，激活函数为softmax
# 如mnist数据集和y值不经过one-hot转化，模型fit时要用sparse_categorical_crossentropy作为损失函数
# 其它离散的分类标签，可以转化为数字索引
from sklearn import preprocessing
enc = preprocessing.OrdinalEncoder()
X = [['class1'], ['class2'],['class3']]
enc.fit(X)
X_transform = enc.transform(X)
print(X_transform)
y_true = [1, 2, 2]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1],[0.05, 0.05, 0.9]]
# Using 'auto'/'sum_over_batch_size' reduction type.
scce = tf.keras.losses.SparseCategoricalCrossentropy()
scce(y_true, y_pred).numpy()
### 4.KL散度KLDivergence
# 相对熵 KLDivergence ，也称KL散度，是连续分布的一种距离度量，通常在离散采样连续输出分布空间上直接回归。
# loss = y_true * log(y_true / y_pred)
y_true = [[0, 1], [0, 0]]
y_pred = [[0.6, 0.4], [0.4, 0.6]]
kl = tf.keras.losses.KLDivergence()
print(kl(y_true, y_pred).numpy())
print(kl(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy())
kl = tf.keras.losses.Poisson(reduction='sum')
print(kl(y_true, y_pred).numpy())
kl = tf.keras.losses.Poisson(reduction='none')
print(kl(y_true, y_pred).numpy())
### 5.泊松损失 Poisson
# 泊松损失 Poisson，适用于符合泊松分布的数据集
y_true = [[0., 1.], [0., 0.]]
y_pred = [[1., 1.], [0., 0.]]
p = tf.keras.losses.Poisson()
print(p(y_true, y_pred).numpy()) # 平均误差
print(p(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()) #带样本权重
p = tf.keras.losses.Poisson(reduction='sum') # 总和
print(p(y_true, y_pred).numpy())
p = tf.keras.losses.Poisson(reduction='none') # 分别计算每个样本的误差
print(p(y_true, y_pred).numpy())
### 6.均方差 Mean Squared Error
# 回归问题，MeanSquaredError计算真实值和预测值之间的误差的平方平均值。
y_true = [[0., 1.], [0., 0.]]
y_pred = [[1., 1.], [1., 0.]]
mse = tf.keras.losses.MeanSquaredError()
print(mse(y_true, y_pred).numpy())
print(mse(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy())
mse = tf.keras.losses.MeanSquaredError(reduction='sum')
print(mse(y_true, y_pred).numpy())
mse = tf.keras.losses.MeanSquaredError(reduction='none')
print(mse(y_true, y_pred).numpy())
### 7.均方对数误差 Mean Squared Logarithmic Error
# loss = square(log(y_true + 1.) - log(y_pred + 1.))
y_true = [[0., 1.], [0., 0.]]
y_pred = [[1., 1.], [1., 0.]]
msle = tf.keras.losses.MeanSquaredLogarithmicError()
print(msle(y_true, y_pred).numpy())
print(msle(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy())
msle = tf.keras.losses.MeanSquaredLogarithmicError(reduction='sum')
print(msle(y_true, y_pred).numpy())
msle = tf.keras.losses.MeanSquaredLogarithmicError(reduction='none')
print(msle(y_true, y_pred).numpy())
### 8.自定义损失函数
def get_loss(y_pre,y_input):
pass参考：
https://tensorflow.google.cn/api_docs/python/tf/keras/losses/Loss