运行自动编码器拟合时，keras自动编码器模型中的Conv1D输出形状不正确。

我尝试使用keras自动编码器模型来压缩和解压缩我的时间序列数据。但当我用Conv1D更改图层时，输出形状不正确。
我有一些形状为（4000，689）的时间序列数据，其中表示4000个样本，每个样本有689个特征。我想使用Conv1D来压缩数据，但Upsampling层和最后一个Conv1D层的输出形状（？，688，1）不等于输入形状（，689，1）。
我应该如何设置这些图层的参数？

x_train = data[0:4000].values
x_test = data[4000:].values
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)

x_train形状：（四千，六百八十九）
x_测试图形：（第二百零二章，六百八十九）
我将x_train，x_test重新调整为3dim，如下所示。

x_tr = x_train.reshape(4000,689,1)
x_te = x_test.reshape(202,689,1)
print('x_tr shape:', x_tr.shape)
print('x_te shape:', x_te.shape)

x_tr图形：（4000，689，1）
x_te图形：（202，689，1）

input_img = Input(shape=(689,1))

x = Conv1D(16, 3, activation='relu', padding='same')(input_img)
print(x)
x = MaxPooling1D(2, padding='same')(x)
print(x)
x = Conv1D(8, 3, activation='relu', padding='same')(x)
print(x)
x = MaxPooling1D(2, padding='same')(x)
print(x)
x = Conv1D(8, 3, activation='relu', padding='same')(x)
print(x)
encoded = MaxPooling1D(2)(x)
print(encoded)
print('--------------')
    
    
x = Conv1D(8, 3, activation='relu', padding='same')(encoded)
print(x)
x = UpSampling1D(2)(x)
print(x)
x = Conv1D(8, 3, activation='relu', padding='same')(x)
print(x)
x = UpSampling1D(2)(x)
print(x)
x = Conv1D(16, 3, activation='relu', padding='same')(x)
print(x)
x = UpSampling1D(2)(x)
print(x)
decoded = Conv1D(1, 3, activation='sigmoid', padding='same')(x)
print(decoded)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='mse')

当我导入这些模型并在Jupyter中运行上面的单元格时，它看起来不错。也许吧。但是当我运行autoencoder.fit时，我在下一个代码中得到了错误。

autoencoder.fit(x_tr, x_tr, epochs=50, batch_size=128, shuffle=True, validation_data=(x_te, x_te))

所以我每层print。
各层' print'结果如下。

Tensor("conv1d_166/Relu:0", shape=(?, 689, 16), dtype=float32)
Tensor("max_pooling1d_71/Squeeze:0", shape=(?, 345, 16), dtype=float32)
Tensor("conv1d_167/Relu:0", shape=(?, 345, 8), dtype=float32)
Tensor("max_pooling1d_72/Squeeze:0", shape=(?, 173, 8), dtype=float32)
Tensor("conv1d_168/Relu:0", shape=(?, 173, 8), dtype=float32)
Tensor("max_pooling1d_73/Squeeze:0", shape=(?, 86, 8), dtype=float32)

Tensor("conv1d_169/Relu:0", shape=(?, 86, 8), dtype=float32)
Tensor("up_sampling1d_67/concat:0", shape=(?, 172, 8), dtype=float32)
Tensor("conv1d_170/Relu:0", shape=(?, 172, 8), dtype=float32)
Tensor("up_sampling1d_68/concat:0", shape=(?, 344, 8), dtype=float32)
Tensor("conv1d_171/Relu:0", shape=(?, 344, 16), dtype=float32)
Tensor("up_sampling1d_69/concat:0", shape=(?, 688, 16), dtype=float32)
Tensor("conv1d_172/Sigmoid:0", shape=(?, 688, 1), dtype=float32)

以下值错误：

ValueError                                Traceback (most recent call last)
<ipython-input-74-56836006a800> in <module>
      3                 batch_size=128,
      4                 shuffle=True,
----> 5                 validation_data=(x_te, x_te)
      6                 )

~/anaconda3/envs/keras/lib/python3.6/site-packages/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, **kwargs)
    950             sample_weight=sample_weight,
    951             class_weight=class_weight,
--> 952             batch_size=batch_size)
    953         # Prepare validation data.
    954         do_validation = False

~/anaconda3/envs/keras/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    787                 feed_output_shapes,
    788                 check_batch_axis=False,  # Don't enforce the batch size.
--> 789                 exception_prefix='target')
    790 
    791             # Generate sample-wise weight values given the `sample_weight` and

~/anaconda3/envs/keras/lib/python3.6/site-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    136                             ': expected ' + names[i] + ' to have shape ' +
    137                             str(shape) + ' but got array with shape ' +
--> 138                             str(data_shape))
    139     return data
    140 

ValueError: Error when checking target: expected conv1d_172 to have shape (688, 1) but got array with shape (689, 1)

是floor函数使这种情况发生的吗？
如何正确修复错误和autoencoder.fit？
先谢谢你。