Convolution1D 到 Convolution2D

Question

总结问题 我有一个来自传感器的原始信号，长度为 76000 个数据点。我想要用 CNN 处理这些数据。为此，我想我可以使用 Lambda 层从原始信号形成短时傅立叶变换，例如

x = Lambda(lambda v: tf.abs(tf.signal.stft(v,frame_length=frame_length,frame_step=frame_step)))(x)

这完全有效。但我想更进一步，提前处理Raw数据。希望 Convolution1D 层充当过滤器，让一些频率通过并阻止其他频率。

我试过的 我确实有两个独立的（原始数据处理的 Conv1D 示例和我处理 STFT "image" 的 Conv2D 示例）向上和运行。但是我想结合这些。

Conv1D 其中输入为：input = Input(shape = (76000,))

  x = Lambda(lambda v: tf.expand_dims(v,-1))(input)
  x = layers.Conv1D(filters =10,kernel_size=100,activation = 'relu')(x)
  x = Flatten()(x)
  output = Model(input, x)

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 76000)]           0         
_________________________________________________________________
lambda_2 (Lambda)            (None, 76000, 1)          0         
_________________________________________________________________
conv1d (Conv1D)              (None, 75901, 10)         1010      
________________________________________________________________

Conv2D 相同输入

  x = Lambda(lambda v:tf.expand_dims(tf.abs(tf.signal.stft(v,frame_length=frame_length,frame_step=frame_step)),-1))(input)
  x = BatchNormalization()(x)
Model: "model_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_6 (InputLayer)         [(None, 76000)]           0         
_________________________________________________________________
lambda_8 (Lambda)            (None, 751, 513, 1)       0         
_________________________________________________________________
batch_normalization_3 (Batch (None, 751, 513, 1)       4         
_________________________________________________________________
. . .
. . . 
flatten_4 (Flatten)          (None, 1360)              0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 1360)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 1361

我正在寻找一种方法来组合从 "conv1d" 到 "lambda_8" 层的起点。如果我把它们放在一起，我会得到：

  x = Lambda(lambda v: tf.expand_dims(v,-1))(input)
  x = layers.Conv1D(filters =10,kernel_size=100,activation = 'relu')(x)
  #x = Flatten()(x)
  x = Lambda(lambda v:tf.expand_dims(tf.abs(tf.signal.stft(v,frame_length=frame_length,frame_step=frame_step)),-1))(x)
Layer (type)                 Output Shape              Param #   
=================================================================
input_6 (InputLayer)         [(None, 76000)]           0         
_________________________________________________________________
lambda_17 (Lambda)           (None, 76000, 1)          0         
_________________________________________________________________
conv1d_6 (Conv1D)            (None, 75901, 10)         1010      
_________________________________________________________________
lambda_18 (Lambda)           (None, 75901, 0, 513, 1)  0         <-- Wrong
=================================================================

这不是我要找的。它应该看起来更像 (None,751,513,10,1)。到目前为止，我找不到合适的解决方案。有人可以帮助我吗？

提前致谢！

Answer 1

从the documentation看来，stft似乎只接受(..., length)输入，不接受(..., length, channels)。

因此，第一个建议是先将通道移动到另一个维度，以保持最后一个索引处的长度并使功能正常工作。

现在，当然，您需要匹配长度，不能将 76000 与 75901 匹配。因此第二个建议是在一维卷积中使用 padding='same' 以保持长度相等。

最后，由于 stft 的结果中已经有 10 个通道，因此不需要在最后一个 lambda 中扩展 dims。

总结：

一维部分

inputs = Input((76000,)) #(batch, 76000)

c1Out = Lambda(lambda x: K.expand_dims(x, axis=-1))(inputs) #(batch, 76000, 1)
c1Out = Conv1D(10, 100, activation = 'relu', padding='same')(c1Out) #(batch, 76000, 10)

#permute for putting length last, apply stft, put the channels back to their position
c1Stft = Permute((2,1))(c1Out) #(batch, 10, 76000)
c1Stft = x = Lambda(lambda v: tf.abs(tf.signal.stft(v,
                                                    frame_length=frame_length,
                                                    frame_step=frame_step)
                                     )
                    )(c1Stft) #(batch, 10, probably 751, probably 513)
c1Stft = Permute((2,3,1))(c1Stft) #(batch, 751, 513, 10)

二维部分，你的代码似乎没问题：

c2Out = Lambda(lambda v: tf.expand_dims(tf.abs(tf.signal.stft(v,
                                                              frame_length=frame_length,
                                                              frame_step=frame_step)
                                               ),
                                        -1))(inputs) #(batch, 751, 513, 1)

现在一切都具有兼容的尺寸

#maybe
#c2Out = Conv2D(10, ..., padding='same')(c2Out) 

joined = Concatenate()([c1Stft, c2Out]) #(batch, 751, 513, 11) #maybe (batch, 751, 513, 20)

further = BatchNormalization()(joined)
further = Conv2D(...)(further)

警告：我不知道他们是否使 stft 可微，Conv1D 部分仅在定义了渐变时才有效。

Convolution1D 到 Convolution2D

Convolution1D to Convolution2D

conv-neural-network

keras

tensorflow2.0