Python tf.keras.losses.MeanSquaredLogarithmicError用法及代码示例

计算 y_true 和 y_pred 之间的均方对数误差。

继承自：Loss

用法

tf.keras.losses.MeanSquaredLogarithmicError(
    reduction=losses_utils.ReductionV2.AUTO,
    name='mean_squared_logarithmic_error'
)

loss = square(log(y_true + 1.) - log(y_pred + 1.))

单机使用：

y_true = [[0., 1.], [0., 0.]]
y_pred = [[1., 1.], [1., 0.]]
# Using 'auto'/'sum_over_batch_size' reduction type.
msle = tf.keras.losses.MeanSquaredLogarithmicError()
msle(y_true, y_pred).numpy()
0.240

# Calling with 'sample_weight'.
msle(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()
0.120

# Using 'sum' reduction type.
msle = tf.keras.losses.MeanSquaredLogarithmicError(
    reduction=tf.keras.losses.Reduction.SUM)
msle(y_true, y_pred).numpy()
0.480

# Using 'none' reduction type.
msle = tf.keras.losses.MeanSquaredLogarithmicError(
    reduction=tf.keras.losses.Reduction.NONE)
msle(y_true, y_pred).numpy()
array([0.240, 0.240], dtype=float32)

compile() API 的用法：

model.compile(optimizer='sgd',
              loss=tf.keras.losses.MeanSquaredLogarithmicError())