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。但是当我使用
Conv1D更改图层时，输出形状不正确。我有一些时间序列数据，形状为（4000，689），其中代表4000个样本，每个样本具有689个特征。我想使用
Conv1D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） 。如何设置这些图层的参数？预先感谢。
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火车形状：（4000，689）
x_test形状：（202，689）
我将x_train，x_test重塑为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形状：（4000、689、1）
x_te形状：（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，看起来还可以。也许。但是在运行
autoencoder.fit时，我在下一个代码中收到错误。
autoencoder.fit(x_tr, x_tr, epochs=50, batch_size=128, shuffle=True, validation_data=(x_te, x_te))
因此，我print每层。
下面各层的
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)
ValueError下面：
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)
floor函数是否可以实现？
如何正确纠正错误和autoencoder.fit？
预先感谢。