Machine learning 使用自动编码器的1的不兼容形状

Machine learning 使用自动编码器的1的不兼容形状,machine-learning,keras,deep-learning,autoencoder,Machine Learning,Keras,Deep Learning,Autoencoder,我正在尝试在时间序列上使用自动编码器。当我在数据上使用填充时,一切都正常,但当我使用可变数据长度时,我有一些小的数据形状问题:不兼容的形状:[1125,4]与[1126,4] input_series=input(shape=(无,4)) x=Conv1D(4,2,激活='relu',填充='same')(输入\序列) x=maxpoolg1d(1,padding='same')(x) x=Conv1D(4,3,激活='relu',填充='same')(x) x=maxpoolg1d(1,pad

我正在尝试在时间序列上使用自动编码器。当我在数据上使用填充时,一切都正常,但当我使用可变数据长度时,我有一些小的数据形状问题:
不兼容的形状:[1125,4]与[1126,4]

input_series=input(shape=(无,4))
x=Conv1D(4,2,激活='relu',填充='same')(输入\序列)
x=maxpoolg1d(1,padding='same')(x)
x=Conv1D(4,3,激活='relu',填充='same')(x)
x=maxpoolg1d(1,padding='same')(x)
x=Conv1D(4,3,激活='relu',填充='same')(x)
编码器=maxpoolg1d(1,padding='same',name='encoder')(x)
x=Conv1D(4,3,激活='relu',填充='same')(编码器)
x=上采样1d(1)(x)
x=Conv1D(4,3,激活='relu',填充='same')(x)
x=上采样1d(1)(x)
x=Conv1D(16,2,activation='relu')(x)
x=上采样1d(1)(x)
解码器=Conv1D(4,2,激活='sigmoid',填充='same')(x)
自动编码器=型号(输入_系列,解码器)
编译(loss='mse',optimizer='adam')
autoencoder.summary()
总结:

_________________________________________________________________
层(类型)输出形状参数
=================================================================
输入_25(输入层)(无,无,4)0
_________________________________________________________________
conv1d_169(conv1d)(无,无,4)36
_________________________________________________________________
最大池1 d_49(最大池(无,无,4)0
_________________________________________________________________
conv1d_170(conv1d)(无,无,4)52
_________________________________________________________________
最大池1 d_50(最大池(无,无,4)0
_________________________________________________________________
conv1d_171(conv1d)(无,无,4)52
_________________________________________________________________
编码器(MaxPoolg1d)(无,无,4)0
_________________________________________________________________
conv1d_172(conv1d)(无,无,4)52
_________________________________________________________________
上采样1 d_73(上采样(无,无,4)0
_________________________________________________________________
conv1d_173(conv1d)(无,无,4)52
_________________________________________________________________
向上采样1 d_74(向上采样(无,无,4)0
_________________________________________________________________
conv1d_174(conv1d)(无,无,16)144
_________________________________________________________________
上采样1 d_75(上采样(无,无,16)0
_________________________________________________________________
conv1d_175(conv1d)(无,无,4)132
=================================================================
总参数:520
可培训参数:520
不可训练参数:0
_________________________________________________________________
错误:

1/50纪元
---------------------------------------------------------------------------
InvalidArgumentError回溯(最后一次最近调用)
调用中的C:\ProgramData\Anaconda3\lib\site packages\tensorflow\python\client\session.py(self,fn,*args)
1321尝试:
->1322返回fn(*args)
1323除错误外。操作错误为e:
C:\ProgramData\Anaconda3\lib\site packages\tensorflow\python\client\session.py in\u run\fn(feed\u dict、fetch\u list、target\u list、options、run\u元数据)
1306返回self.\u调用\u tf\u会话运行(
->1307选项、提要、获取列表、目标列表、运行元数据)
1308
C:\ProgramData\Anaconda3\lib\site packages\tensorflow\python\client\session.py in\u call\u tf\u sessionrun(self、options、feed\u dict、fetch\u list、target\u list、run\u metadata)
1408自会话、选项、提要、获取列表、目标列表、,
->1409运行单元(元数据)
1410其他:
InvalidArgumentError:不兼容的形状:[1125,4]与[1126,4]
[Node:loss_22/conv1d_175_loss/sub=sub[T=DT_FLOAT,[u class=[“loc:@training_18/Adam/gradients/loss_22/conv1d_175_loss/sub_grad/reforme”],[u device=“/job:localhost/replica:0/任务:0/设备:GPU:0”](conv1d_175/Sigmoid,[u arg_conv1d_conv1d_175_175_目标/[u 4489]
[[Node:loss_22/mul/_4613=\u Recv[client_terminated=false,Recv_device=“/job:localhost/replica:0/task:0/device:CPU:0”,send_device=“/job:localhost/replica:0/task:0/device:GPU:0”,send_device_device_化身=1,tensor_name=“edge_1245_loss_22/mul”,tensor_type=DT_FLOAT,\u device=“/job:localhost/replica:0/task:0/CPU:0”,]
在处理上述异常期间,发生了另一个异常:
InvalidArgumentError回溯(最后一次最近调用)
在()
6列发电机(X列),
7个时代=50,
---->每个历元8步=len(X列))
9
10
包装中的C:\ProgramData\Anaconda3\lib\site packages\keras\legacy\interfaces.py(*args,**kwargs)
89警告。警告('更新您的`+对象\u名称+
90'`对Keras 2 API的调用:'+签名,stacklevel=2)
--->91返回函数(*args,**kwargs)
92包装器._原始函数=func
93返回包装器
C:\ProgramData\Anaconda3\lib\site packages\keras\engine\training.py-in-fit\u生成器(self、生成器、每个历元的步骤、历元、冗余、回调、验证数据、验证步骤、类权重、最大队列大小、工人、使用多处理、无序、初始历元)
2228 outs=批次(x,y,
2229样品重量=样品重量,
->2230级_