我正在嘗試為以下問題創建一個有效的損失函式：
損失是為紅線之間的每個范圍計算的 MAE 的總和。藍線是ground truth，橙線是預測，紅點標記ground truth的值從一個到另一個變化并關閉當前值范圍的索引。輸入值在 [0,1] 范圍內。值范圍的數量不同；它可以在 2-12 之間。
以前，我用 TF map_fn 撰寫了一個代碼，但它非常慢：
def rwmae_old(y_true, y_pred):
y_pred = tf.convert_to_tensor(y_pred)
y_true = tf.cast(y_true, y_pred.dtype)
# prepare array
yt_tmp = tf.concat(
[tf.ones([len(y_true), 1], dtype=y_pred.dtype) * tf.cast(len(y_true), dtype=y_true.dtype), y_true], axis=-1)
yt_tmp = tf.concat([yt_tmp, tf.ones([len(y_true), 1]) * tf.cast(len(y_true), dtype=y_true.dtype)], axis=-1)
# find where there is a change of values between consecutive indices
ranges = tf.transpose(tf.where(yt_tmp[:, :-1] != yt_tmp[:, 1:]))
ranges_cols = tf.concat(
[[0], tf.transpose(tf.where(ranges[1][1:] == 0))[0] 1, [tf.cast(len(ranges[1]), dtype=y_true.dtype)]], axis=0)
ranges_rows = tf.range(len(y_true))
losses = tf.map_fn(
# loop through every row in the array
lambda ii:
tf.reduce_mean(
tf.map_fn(
# loop through every range within the example and calculate the loss
lambda jj:
tf.reduce_mean(
tf.abs(
y_true[ii][ranges[1][ranges_cols[ii] jj]: ranges[1][ranges_cols[ii] jj 1]] -
y_pred[ii][ranges[1][ranges_cols[ii] jj]: ranges[1][ranges_cols[ii] jj 1]]
),
),
tf.range(ranges_cols[ii 1] - ranges_cols[ii] - 1),
fn_output_signature=y_pred.dtype
)
),
ranges_rows,
fn_output_signature=y_pred.dtype
)
return losses
今天，我創建了一個惰性代碼，它只遍歷批處理中的每個示例，并檢查索引之間的值是否發生變化，如果是，則計算當前范圍的 MAE：
def rwmae(y_true, y_pred):
(batch_size, length) = y_pred.shape
losses = tf.zeros(batch_size, dtype=y_pred.dtype)
for ii in range(batch_size):
# reset loss for the current row
loss = tf.constant(0, dtype=y_pred.dtype)
# set current range start index to 0
ris = 0
for jj in range(length - 1):
if y_true[ii][jj] != y_true[ii][jj 1]:
# we found a point of change, calculate the loss in the current range and ...
loss = tf.add(loss, tf.reduce_mean(tf.abs(y_true[ii][ris: jj 1] - y_pred[ii][ris: jj 1])))
# ... update the new range starting point
ris = jj 1
if ris != length - 1:
# we need to calculate the loss for the rest of the vector
loss = tf.add(loss, tf.reduce_mean(tf.abs(y_true[ii][ris: length] - y_pred[ii][ris: length])))
#replace loss in the proper row
losses = tf.tensor_scatter_nd_update(losses, [[ii]], [loss])
return losses
你覺得有什么辦法可以提高它的效率嗎？或者你認為這個問題有更好的損失函式？
uj5u.com熱心網友回復：
你可以嘗試這樣的事情：
import numpy as np
import tensorflow as tf
def rwmae(y_true, y_pred):
(batch_size, length) = tf.shape(y_pred)
losses = tf.zeros(batch_size, dtype=y_pred.dtype)
for ii in tf.range(batch_size):
ris = 0
indices= tf.concat([tf.where(y_true[ii][:-1] != y_true[ii][1:])[:, 0], [length-1]], axis=0)
ragged_indices = tf.ragged.range(tf.concat([[ris], indices[:-1] 1], axis=0), indices 1)
loss = tf.reduce_sum(tf.reduce_mean(tf.abs(tf.gather(y_true[ii], ragged_indices) - tf.gather(y_pred[ii], ragged_indices)), axis=-1, keepdims=True))
losses = tf.tensor_scatter_nd_update(losses, [[ii]], [tf.math.divide_no_nan(loss, tf.cast(tf.shape(indices)[0], dtype=tf.float32))])
return losses
data = np.load('/content/data.npy', allow_pickle=True)
y_pred = data[0:2][0]
y_true = data[0:2][1]
print(rwmae(y_true, y_pred), y_true.shape)
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