在model.fit中使用class_weights略有不同：它实际上更新样本而不是计算加权损失。
我还发现，当x作为TFDataset或生成器传入model.fit时，class_weights和sample_weights在TF 2.0.0中被忽略了。但我相信在TF 2.1.0+中已经修复了这个问题。
这是我针对多热编码标签的加权二元交叉熵函数。
import tensorflow as tf
import tensorflow.keras.backend as K
import numpy as np
def weighted_binary_cross_entropy(weights: dict, from_logits: bool = False):
'''
Return a function for calculating weighted binary cross entropy
It should be used for multi-hot encoded labels
# Example
y_true = tf.convert_to_tensor([1, 0, 0, 0, 0, 0], dtype=tf.int64)
y_pred = tf.convert_to_tensor([0.6, 0.1, 0.1, 0.9, 0.1, 0.], dtype=tf.float32)
weights = {
0: 1.,
1: 2.
}
# with weights
loss_fn = get_loss_for_multilabels(weights=weights, from_logits=False)
loss = loss_fn(y_true, y_pred)
print(loss)
# tf.Tensor(0.6067193, shape=(), dtype=float32)
# without weights
loss_fn = get_loss_for_multilabels()
loss = loss_fn(y_true, y_pred)
print(loss)
# tf.Tensor(0.52158177, shape=(), dtype=float32)
# Another example
y_true = tf.convert_to_tensor([[0., 1.], [0., 0.]], dtype=tf.float32)
y_pred = tf.convert_to_tensor([[0.6, 0.4], [0.4, 0.6]], dtype=tf.float32)
weights = {
0: 1.,
1: 2.
}
# with weights
loss_fn = get_loss_for_multilabels(weights=weights, from_logits=False)
loss = loss_fn(y_true, y_pred)
print(loss)
# tf.Tensor(1.0439969, shape=(), dtype=float32)
# without weights
loss_fn = get_loss_for_multilabels()
loss = loss_fn(y_true, y_pred)
print(loss)
# tf.Tensor(0.81492424, shape=(), dtype=float32)
@param weights A dict setting weights for 0 and 1 label. e.g.
{
0: 1.
1: 8.
}
For this case, we want to emphasise those true (1) label,
because we have many false (0) label. e.g.
[
[0 1 0 0 0 0 0 0 0 1]
[0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 1 0 0 0 0 0]
]
@param from_logits If False, we apply sigmoid to each logit
@return A function to calcualte (weighted) binary cross entropy
'''
assert 0 in weights
assert 1 in weights
def weighted_cross_entropy_fn(y_true, y_pred):
tf_y_true = tf.cast(y_true, dtype=y_pred.dtype)
tf_y_pred = tf.cast(y_pred, dtype=y_pred.dtype)
weights_v = tf.where(tf.equal(tf_y_true, 1), weights[1], weights[0])
weights_v = tf.cast(weights_v, dtype=y_pred.dtype)
ce = K.binary_crossentropy(tf_y_true, tf_y_pred, from_logits=from_logits)
loss = K.mean(tf.multiply(ce, weights_v))
return loss
return weighted_cross_entropy_fn
