what the Conv1D with kernel size equal to 1 do?

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I read an example of using LSTM with CONV1. (Took it from: CNN LSTM)

Conv1D(filters=64, kernel_size=1, activation='relu')
  • I understand that the dimension of the convolutional is 1 (one dim with size 1))
  1. what is the value of the convolution ? (what is the value of the matrix 1*1 ?)
  2. I can't figure out what is the filters=64 ? what does it mean ?
  3. Is the relu activation function work on the output of the convolutional ? (from what I read it seems like that, but I'm not sure)
  4. what is the motivation to use convolutional with kernel_size = 1, as we do here ?
lstm conv-neural-network keras deep-learning convolution
1 Answers

filters

filters = 64 means number of separate filters used is 64. Each filter will output 1 channel. i.e. here 64 filters operate on input to produce 64 different channels(or vectors). Hence filters parameter determines number of output channels.

kernel_size

kernel_size determines the size of the convolution window. Suppose kernel_size = 1 then each kernel will have dimension of in_channels x 1. Hence each kernel weight will be in_channels x 1 dimension tensor.

activation = relu

That means relu activation will be applied on the output of convolution operation.

kernel_size = 1 convolution

Used to reduce depth channels with applying non-linearity. It will do something like weighted average across the channels while keeping receptive field.

In your eg: filters = 64, kernel_size = 1, activation = relu Suppose input feature map has size of 100 x 10(100 channels). Then the layer weight will of dimension 64 x 100 x 1. The output size will be 64 x 10.

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