Python 设置样本“重量”模式=“；“暂时的”；不'；好像不行

我正在tensorflow 2.0中使用LSTM，并尝试为训练样本分配权重（我已经尝试使用class_权重字典，但它抱怨不支持三维数组。我的数组形状是（26000，7，1））

正如文档中所建议的，我在编译中将sample_weight_mode设置为“temporal”，但在这之后，当我尝试拟合模型时，仍然会出现以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-100-1bb94008291c> in <module>
      1 with tf.device("/device:GPU:0"):
----> 2     model.fit(X, y, epochs=500, batch_size=4096, verbose=1, validation_split=0.2, sample_weight=cw.reshape(26000,7,1))

C:\ProgramData\Anaconda3\envs\thesis-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    641         max_queue_size=max_queue_size,
    642         workers=workers,
--> 643         use_multiprocessing=use_multiprocessing)
    644 
    645   def evaluate(self,

C:\ProgramData\Anaconda3\envs\thesis-gpu\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
    630         steps=steps_per_epoch,
    631         validation_split=validation_split,
--> 632         shuffle=shuffle)
    633 
    634     if validation_data:

C:\ProgramData\Anaconda3\envs\thesis-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
   2459           training_utils.standardize_weights(ref, sw, cw, mode)
   2460           for (ref, sw, cw, mode) in zip(y, sample_weights, class_weights,
-> 2461                                          feed_sample_weight_modes)
   2462       ]
   2463       # Check that all arrays have the same length.

C:\ProgramData\Anaconda3\envs\thesis-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py in <listcomp>(.0)
   2458       sample_weights = [
   2459           training_utils.standardize_weights(ref, sw, cw, mode)
-> 2460           for (ref, sw, cw, mode) in zip(y, sample_weights, class_weights,
   2461                                          feed_sample_weight_modes)
   2462       ]

C:\ProgramData\Anaconda3\envs\thesis-gpu\lib\site-packages\tensorflow\python\keras\engine\training_utils.py in standardize_weights(y, sample_weight, class_weight, sample_weight_mode)
    839     if sample_weight is not None and len(sample_weight.shape) != 1:
    840       raise ValueError('Found a sample_weight array with shape ' +
--> 841                        str(sample_weight.shape) + '. '
    842                        'In order to use timestep-wise sample weights, '
    843                        'you should specify '

ValueError: Found a sample_weight array with shape (26000, 7, 1). In order to use timestep-wise sample weights, you should specify sample_weight_mode="temporal" in compile(). If you just mean to use sample-wise weights, make sure your sample_weight array is 1D.

---

样本加权适用于输出（因为你所加权的是损失！），而不是输入，所以你的输入维度是什么并不重要。错误消息明确与输出尺寸有关。

样本重量的尺寸不能大于2。如果

compile

函数中的

sample\u weight\u mode

为

None

，则

sample\u weight

必须为一维。如果

compile

函数中的

sample\u weight\u mode

是

'temporal'

，则

sample\u weight

必须是二维的。 此外，

y.shape[：sample\u weight.ndim]==sample\u weight.shape

始终是正确的。 有关更多详细信息，请参阅

keras.engine.training\u utils.standarized\u weights

的源代码

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import math

# Import TensorFlow
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop, Adam, SGD
from tensorflow.keras.layers import SimpleRNN, LSTM, Dense
from tensorflow.keras.models import Sequential

from sklearn.utils import class_weight

class_weights = class_weight.compute_class_weight('balanced',
                                                 np.unique(y_train),
                                                 y_train)

cw = class_weight.compute_sample_weight({False:1, True:50},#'balanced',
                                                 #np.unique(y_train),
                                                 y.flatten())

model = Sequential()
model.add(LSTM(160, return_sequences=True, dropout=0.05))
model.add(LSTM(80, return_sequences=True, dropout=0.05, activation='relu'))
model.add(LSTM(40, return_sequences=True, dropout=0.05, activation='relu'))
model.add(LSTM(10, return_sequences=True, dropout=0.05))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc', tf.keras.metrics.AUC()], sample_weight_mode="temporal")

with tf.device("/device:GPU:0"):
    model.fit(X, y, epochs=500, batch_size=4096, verbose=1, validation_split=0.2, sample_weight=cw.reshape(26000,7,1))