Caffe Convolution "Pad" parameter conversion to Keras Conv2D

[pmpa...@gmail.com]

I am trying to convert a Caffe-model (in prototxt) to its equivalent in Keras.

One convolutional layer has the following attributes :

layer {

  name: "conv2"

  type: "Convolution"

  bottom: "norm1"

  top: "conv2"

  param {

    lr_mult: 1

    decay_mult: 1

  }

  param {

    lr_mult: 2

    decay_mult: 0

  }

  convolution_param {

    num_output: 384

    kernel_size: 5

   pad : 1

   group: 2

    stride: 2

    weight_filler {

      type: "gaussian"

      std: 0.01

    }

    bias_filler {

      type: "constant"

      value: 0.1

    }

  }

}

My question is how can I implement this parameter in Keras syntax ( using Conv2D layer ) ?

I know that pad='same' may be used, but how can I define that i want 1 pixel around each input, as defined in Caffe?

[Sergey O.]

From Caffe:
pad (or pad_h and pad_w) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input

Since this is done at the input (before doing actual convolution), you can use kera's zeropadding layer:

https://keras.io/layers/convolutional/#zeropadding1d

https://keras.io/layers/convolutional/#zeropadding2d

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[pmpa...@gmail.com]

Thank you sokrypton for your quick response.

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