我第一次嘗試撰寫自定義損失函式。我的模型生成一個時間序列資料，我想要一個損失函式，它會比早期的錯誤更多地懲罰系列后期的錯誤。類似于張量指數用于確定懲罰的地方。張量具有以下結構。
y_true <tf.Tensor 'IteratorGetNext:1' shape=(None, 48, 1) dtype=float32>
y_pred <tf.Tensor 'ResNet34/dense_1/BiasAdd:0' shape=(None, 48, 1) dtype=float32>
我應該怎么做才能使懲罰成為索引的函式？
def custom_loss_function(y_true, y_pred):
squared_difference = tf.square(y_true - y_pred) * 'sqrt(tensor_index)' <- Desired part
return tf.reduce_mean(squared_difference, axis=-1)
uj5u.com熱心網友回復：
也許嘗試使用tf.linspace：
import tensorflow as tf
y_true = tf.random.normal((1, 48, 1))
y_pred = tf.random.normal((1, 48, 1))
def custom_loss_function(y_true, y_pred):
penalty = tf.cast(tf.linspace(start = 1, stop = 5, num = y_pred.shape[1]), dtype=tf.float32)
print(penalty)
squared_difference = tf.square(y_true - y_pred) * tf.expand_dims(penalty, axis=-1)
return tf.reduce_mean(squared_difference, axis=-1)
print(custom_loss_function(y_true, y_pred))
tf.Tensor(
[1. 1.0851064 1.1702127 1.2553191 1.3404255 1.4255319 1.5106384
1.5957447 1.6808511 1.7659575 1.8510638 1.9361702 2.0212767 2.106383
2.1914895 2.2765958 2.3617022 2.4468086 2.531915 2.6170213 2.7021277
2.787234 2.8723404 2.9574468 3.0425532 3.1276596 3.212766 3.2978723
3.3829787 3.468085 3.5531914 3.6382978 3.7234042 3.8085105 3.893617
3.9787233 4.06383 4.1489363 4.2340426 4.319149 4.4042554 4.489362
4.574468 4.6595745 4.744681 4.8297873 4.9148936 5. ], shape=(48,), dtype=float32)
tf.Tensor(
[[1.3424503e 00 1.7936407e 00 9.5141016e-02 4.1933870e-01 2.9060142e-02
1.6663458e 00 3.7182972e 00 2.3884547e-01 1.6393075e 00 9.8062935e 00
1.4726014e 00 6.4087069e-01 1.4197667e 00 2.7730075e-01 2.6717324e 00
1.2410884e 01 2.8422637e 00 2.2836231e 01 1.9438576e 00 7.2612977e-01
2.9226139e 00 1.3040878e 01 5.8225789e 00 2.3456068e 00 2.8281093e 00
4.2308202e 00 2.6682162e 00 4.0025130e-01 3.5946998e-01 8.0574770e-03
2.7833527e-01 3.8349494e-01 7.1913116e-02 3.0325607e-03 5.8022089e 00
4.4835452e-02 4.7429881e 00 6.4035267e-01 5.0330186e 00 2.7156603e 00
1.2085355e-01 3.5016473e-02 7.9860941e-02 3.1455503e 01 5.3314602e 01
3.8006527e 01 1.1620968e 01 4.1495290e 00]], shape=(1, 48), dtype=float32)
更新 1：
import tensorflow as tf
y_true = tf.random.normal((2, 48, 1))
y_pred = tf.random.normal((2, 48, 1))
def custom_loss_function(y_true, y_pred):
penalty = tf.cast(tf.linspace(start = 1, stop = 5, num = tf.shape(y_pred)[1]), dtype=tf.float32)
penalty = tf.expand_dims(penalty, axis=-1)
penalty = tf.expand_dims(tf.transpose(tf.repeat(penalty, repeats=tf.shape(y_pred)[0], axis=1)), axis=-1)
squared_difference = tf.square(y_true - y_pred) * penalty
return tf.reduce_mean(squared_difference, axis=-1)
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