LSTM--在滑动窗口数据上进行预测

Question

我的训练数据是用户日常数据的重叠滑动窗口.它是 形状是 (1470, 3, 256, 18): 1470 批次 3 天的数据，每天有 256 样品 18 各自的特点。

我的目标 形状是 (1470,):每个批次的标签值。

我想训练一个LSTM来预测一个 [3 days batch] -> [one target] 256天的样本中，缺失256个样本的天数用-10填充。

---

我写了以下代码来建立模型。

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dropout,Dense,Masking,Flatten
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.callbacks import TensorBoard,ModelCheckpoint
from tensorflow.keras import metrics

def build_model(num_samples, num_features):

  opt = RMSprop(0.001) 

  model = Sequential()
  model.add(Masking(mask_value=-10., input_shape=(num_samples, num_features)))
  model.add(LSTM(32, return_sequences=True, activation='tanh'))
  model.add(Dropout(0.3))
  model.add(LSTM(16, return_sequences=False, activation='tanh'))
  model.add(Dropout(0.3))
  model.add(Dense(16, activation='tanh'))
  model.add(Dense(8, activation='tanh'))
  model.add(Dense(1))
  model.compile(loss='mse', optimizer=opt ,metrics=['mae','mse'])
  return model

---

model = build_model(256,18)
model.summary()

Model: "sequential_7"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
masking_7 (Masking)          (None, 256, 18)           0         
_________________________________________________________________
lstm_14 (LSTM)               (None, 256, 32)           6528      
_________________________________________________________________
dropout_7 (Dropout)          (None, 256, 32)           0         
_________________________________________________________________
lstm_15 (LSTM)               (None, 16)                3136      
_________________________________________________________________
dropout_8 (Dropout)          (None, 16)                0         
_________________________________________________________________
dense_6 (Dense)              (None, 16)                272       
_________________________________________________________________
dense_7 (Dense)              (None, 8)                 136       
_________________________________________________________________
dense_8 (Dense)              (None, 1)                 9         
=================================================================
Total params: 10,081
Trainable params: 10,081
Non-trainable params: 0
_________________________________________________________________

我可以看到这些形状是不兼容的 但我不知道如何修改代码来解决我的问题。

任何帮助将是感激的

更新。 我把我的数据改成了这样的形状

train_data.reshape(1470*3, 256, 18)

是这样的吗？

Answer 1

我想你要找的是TimeDistributed(LSTM(...))(源头)

day, num_samples, num_features = 3, 256, 18

model = Sequential()
model.add(Masking(mask_value=-10., input_shape=(day, num_samples, num_features)))
model.add(TimeDistributed(LSTM(32, return_sequences=True, activation='tanh')))
model.add(Dropout(0.3))
model.add(TimeDistributed(LSTM(16, return_sequences=False, activation='tanh')))
model.add(Dropout(0.3))
model.add(Dense(16, activation='tanh'))
model.add(Dense(8, activation='tanh'))
model.add(Dense(1))
model.compile(loss='mse', optimizer='adam' ,metrics=['mae','mse'])

model.summary()