我尝试在Keras中使用LSTM进行深度学习。我使用一些信号作为输入（nb_sig），这些信号在训练过程中可能会发生变化，样本数量固定（nb_sample）。我想进行参数识别，因此我的输出层是我的参数编号的大小（nb_param）
因此我创建了大小为（nb_sig x nb_sample）和标签为（nb_param x nb_sample）的训练集
我的问题是我找不到深度学习模型的正确维度。我尝试了这个：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM

nb_sample = 500
nb_sig = 100 # number that may change during the training
nb_param = 10

train = np.random.rand(nb_sig,nb_sample)
label = np.random.rand(nb_sig,nb_param)

print(train.shape,label.shape)
DLmodel = Sequential()
DLmodel.add(LSTM(units=nb_sample, return_sequences=True, input_shape =(None,nb_sample), activation='tanh'))
DLmodel.add(Dense(nb_param, activation="linear", kernel_initializer="uniform"))

DLmodel.compile(loss='mean_squared_error', optimizer='RMSprop', metrics=['accuracy', 'mse'], run_eagerly=True)
print(DLmodel.summary())

DLmodel.fit(train, label, epochs=10, batch_size=nb_sig)

但我收到了以下错误消息：

Traceback (most recent call last):
  File "C:\Users\maxime\Desktop\SESAME\PycharmProjects\LargeScale_2022_09_07\di3.py", line 22, in <module>
    DLmodel.fit(train, label, epochs=10, batch_size=nb_sig)
  File "C:\Python310\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\Python310\lib\site-packages\keras\engine\input_spec.py", line 232, in assert_input_compatibility
    raise ValueError(
ValueError: Exception encountered when calling layer "sequential" "                 f"(type Sequential).

Input 0 of layer "lstm" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (100, 500)

Call arguments received by layer "sequential" "                 f"(type Sequential):
  • inputs=tf.Tensor(shape=(100, 500), dtype=float32)
  • training=True
  • mask=None

我不明白我应该把什么作为LSTM层的input_shape，因为我在训练期间使用的信号数量会改变，这对我来说不是很清楚。