你好，我是LSTM模型的新手，我需要一些帮助。我有一个数据集，我的X向量中有一些单词我想用LSTM嵌入层来预测我的y向量它包含0-1之间的值。我见过很多例子，其中y向量由类组成，我们得到一个分类问题，但在这个回归问题中，我找不到解决方案。
我使用下面的代码。首先，我导入了一些库，然后尝试使用简单的LSTM深度网络来预测y。此外，正如您所看到的，我使用keras嵌入层来预测y值
import numpy as np
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Input, Embedding, LSTM, SpatialDropout1D, Dense, Dropout
from tensorflow.keras.models import Model, Sequential```
you can see the X and y vectors
X_train
array(['sinus surgery yesterday recover binging game throne start beginning hope not long mend',
'respect game throne good fight scene week barry like far generally amazing episode television bill hader direction win Emmi offend wanton consumerism game throne oreos going eat nonstop remove face earth face end humanity human game throne wait night battle actually lie plan realistic far type game throne viewer happen happen end big winner game throne viewer sibling write article night game throne episode social medium absolute core game throne experience point watch thing time woman quickly accumulate power rule man mistake dead uncontrollable kid breathe fire game throne realistic family nerd weirdo end run world game throne basically story silicon valley game throne completely wipe trump trend story list winter feel good english major let offer insight subtle foreshadowing game throne threat winter come battle winterfell stoner guide game throne minute series end start episode feel like new weekend family role play game throne ramsey wife cersei kid joffrey finally way people internet impossible open page scroll timeline game throne spoiler minute east coast release finally kick online habit come popular news list game throne subject rise petty politic trump tantrum remain air good nation winner game throne officially',
'wow people sign petition remake game throne season competent writer petition',
...,
'star war rian johnson game throne creator work new trilogy honestly not mind bit think work fantastic new trilogy',
'watch game throne documentary tonight',
'game throne trash glad tell people read book'], dtype=object)
y_train
array([0.23024851, 0.47096001, 0.2237812 , ..., 0.07742384, 0.05732593,
0.06153279])```
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X_train)
max_length = max([len(s) for s in X_train])
vocab_size = len(tokenizer.word_index) + 1
X_train_tokens = tokenizer.texts_to_sequences(X_train)
X_train_pad = pad_sequences(X_train_tokens,
maxlen= max_length, padding= 'post')
embedding_dim = 100
model = Sequential()
model.add(Embedding(vocab_size, embedding_dim, input_length = max_length))
model.add(SpatialDropout1D(0.25))
model.add(LSTM(80, dropout=0.5))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='mse',optimizer='rmsprop')
print(model.summary())
model.fit( X_train_pad, y_train, validation_split=0.2, batch_size= 64, epochs= 20)
Epoch 1/20
5031/5031 [==============================] - 612s 120ms/step - loss: 0.0062 - val_loss: 0.0055
Epoch 2/20
5031/5031 [==============================] - 603s 120ms/step - loss: 0.0056 - val_loss: 0.0057
Epoch 3/20
5031/5031 [==============================] - 601s 119ms/step - loss: 0.0056 - val_loss: 0.0060
Epoch 4/20
5031/5031 [==============================] - 603s 120ms/step - loss: 0.0056 - val_loss: 0.0055
Epoch 5/20
5031/5031 [==============================] - 601s 119ms/step - loss: 0.0056 - val_loss: 0.0057
Epoch 6/20
5031/5031 [==============================] - 602s 120ms/step - loss: 0.0055 - val_loss: 0.0060
Epoch 7/20
5031/5031 [==============================] - 600s 119ms/step - loss: 0.0056 - val_loss: 0.0060
Epoch 8/20
5031/5031 [==============================] - 603s 120ms/step - loss: 0.0056 - val_loss: 0.0058
Epoch 9/20
5031/5031 [==============================] - 601s 120ms/step - loss: 0.0056 - val_loss: 0.0055
Epoch 10/20
5031/5031 [==============================] - 607s 121ms/step - loss: 0.0055 - val_loss: 0.0057
Epoch 11/20
5031/5031 [==============================] - 604s 120ms/step - loss: 0.0056 - val_loss: 0.0056
Epoch 12/20
5031/5031 [==============================] - 600s 119ms/step - loss: 0.0055 - val_loss: 0.0059
Epoch 13/20
5031/5031 [==============================] - 600s 119ms/step - loss: 0.0056 - val_loss: 0.0059
Epoch 14/20
5031/5031 [==============================] - 605s 120ms/step - loss: 0.0056 - val_loss: 0.0062
Epoch 15/20
5031/5031 [==============================] - 605s 120ms/step - loss: 0.0056 - val_loss: 0.0055
Epoch 16/20
5031/5031 [==============================] - 602s 120ms/step - loss: 0.0055 - val_loss: 0.0056
Epoch 17/20
5031/5031 [==============================] - 604s 120ms/step - loss: 0.0055 - val_loss: 0.0059
Epoch 18/20
5031/5031 [==============================] - 601s 119ms/step - loss: 0.0054 - val_loss: 0.0058
Epoch 19/20
5031/5031 [==============================] - 599s 119ms/step - loss: 0.0056 - val_loss: 0.0057
Epoch 20/20
5031/5031 [==============================] - 599s 119ms/step - loss: 0.0056 - val_loss: 0.0054
正如您所看到的，验证损失非常小，当我尝试预测某些值时，我的预测值与预测值相同。我该怎么修理它?有可能用词嵌入预测连续向量吗?还是只能预测类?
我已经阅读了您显示的整个代码，并且可以指出您在这里遗漏了一行:
model.add(Dense(1, activation='sigmoid')).
这里我们需要activation = 'linear'，因为您正在进行回归，或者保留未定义的激活参数，因为默认情况下是线性的。