Euclidean Loss as a Python Layer

euclid.prototxt

name: 'EuclideanExample'
layer {
  type: 'DummyData'
  name: 'x'
  top: 'x'
  dummy_data_param {
    shape: { dim: 10 dim: 3 dim: 2 }
    data_filler: { type: 'gaussian' }
  }
}
layer {
  type: 'DummyData'
  name: 'y'
  top: 'y'
  dummy_data_param {
    shape: { dim: 10 dim: 3 dim: 2 }
    data_filler: { type: 'gaussian' }
  }
}
# include InnerProduct layers for parameters
# so the net will need backward
layer {
  type: 'InnerProduct'
  name: 'ipx'
  top: 'ipx'
  bottom: 'x'
  inner_product_param {
    num_output: 10
    weight_filler { type: 'xavier' }
  }
}
layer {
  type: 'InnerProduct'
  name: 'ipy'
  top: 'ipy'
  bottom: 'y'
  inner_product_param {
    num_output: 10
    weight_filler { type: 'xavier' }
  }
}
layer {
  type: 'Python'
  name: 'loss'
  top: 'loss'
  bottom: 'ipx'
  bottom: 'ipy'
  python_param {
    # the module name -- usually the filename -- that needs to be in $PYTHONPATH
    module: 'pyloss'
    # the layer name -- the class name in the module
    layer: 'EuclideanLossLayer'
  }
  # set loss weight so Caffe knows this is a loss layer
  loss_weight: 1
}

pyloss.py

import caffe
import numpy as np

class EuclideanLossLayer(caffe.Layer):

    def setup(self, bottom, top):
        # check input pair
        if len(bottom) != 2:
            raise Exception("Need two inputs to compute distance.")

    def reshape(self, bottom, top):
        # check input dimensions match
        if bottom[0].count != bottom[1].count:
            raise Exception("Inputs must have the same dimension.")
        # difference is shape of inputs
        self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
        # loss output is scalar
        top[0].reshape(1)

    def forward(self, bottom, top):
        self.diff[...] = bottom[0].data - bottom[1].data
        top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.

    def backward(self, top, propagate_down, bottom):
        for i in range(2):
            if not propagate_down[i]:
                continue
            if i == 0:
                sign = 1
            else:
                sign = -1
            bottom[i].diff[...] = sign * self.diff / bottom[i].num

no more errors... going to try to turn this into an actual learning example, and see if it works properly

import caffe
import numpy as np

class PythonDummyData(caffe.Layer):
    def setup(self, bottom, top):
        assert(len(top)==1) # one output

    def reshape(self, bottom, top):
        top[0].reshape(10,3,2)

    def forward(self, bottom, top):
        top[0].data[...] = np.random.randn(10, 3, 2)

    def backward(self,top, propagate_down, bottom):
        pass

Hello, I am trying to use such python layer but I can't.
I am getting this exception:
I1007 17:48:31.366592 30357 layer_factory.hpp:77] Creating layer loss *** Aborted at 1475851711 (unix time) try "date -d @1475851711" if you are using GNU date *** PC: @ 0x7f32895f1156 (unknown) *** SIGSEGV (@0x0) received by PID 30357 (TID 0x7f328b07fa40) from PID 0; stack trace: *** @ 0x7f328883ecb0 (unknown) @ 0x7f32895f1156 (unknown) @ 0x7f3289b43dfe (unknown) @ 0x7f32429d0d9c google::protobuf::MessageLite::ParseFromArray() @ 0x7f3242a1f652 google::protobuf::EncodedDescriptorDatabase::Add() @ 0x7f32429da012 google::protobuf::DescriptorPool::InternalAddGeneratedFile() @ 0x7f3242a2b33e google::protobuf::protobuf_AddDesc_google_2fprotobuf_2fdescriptor_2eproto() @ 0x7f3242a5aa75 google::protobuf::StaticDescriptorInitializer_google_2fprotobuf_2fdescriptor_2eproto::StaticDescriptorInitializer_google_2fprotobuf_2fdescriptor_2eproto() @ 0x7f3242a56beb __static_initialization_and_destruction_0() @ 0x7f3242a56c00 _GLOBAL__sub_I_descriptor.pb.cc @ 0x7f328aeca10a (unknown) @ 0x7f328aeca1f3 (unknown) @ 0x7f328aecec30 (unknown) @ 0x7f328aec9fc4 (unknown) @ 0x7f328aece37b (unknown) @ 0x7f327d91b02b (unknown) @ 0x7f328aec9fc4 (unknown) @ 0x7f327d91b62d (unknown) @ 0x7f327d91b0c1 (unknown) @ 0x7f3288f412ae (unknown) @ 0x7f3288f09dae (unknown) @ 0x7f3288f88729 (unknown) @ 0x7f3288ebccbf (unknown) @ 0x7f3288f81d66 (unknown) @ 0x7f3288e47a3f (unknown) @ 0x7f3288f12d43 (unknown) @ 0x7f3288f8b577 (unknown) @ 0x7f3288f6dc13 (unknown) @ 0x7f3288f7154d (unknown) @ 0x7f3288f71682 (unknown) @ 0x7f3288f71a2c (unknown) @ 0x7f3288f88016 (unknown) Segmentation fault (core dumped)

And I really don't know what to do... Could you suggest anything?