Myujth

I am trying to modify the weight of convolution like this.
To do this, I make, initialize my parameters(weight, bias), convolute input image using them.
But, it shows an error because my parameters are not arguments in symbol.

How to add my parameters to arguments in symbol?
If you let me know, I would be very grateful.

If you want to pass in arguments to a Custom Operator you have to do so via the init method.

From https://github.com/apache/incubator-mxnet/issues/5580 here's a snippet illustrating what you need:

class Softmax(mx.operator.CustomOp):

 def __init__(self, xxx, yyy): # arguments xxx, and yyy
 self.xxx = xxx
 self.yyy = yyy
 def forward(self, is_train, req, in_data, out_data, aux):
 x = in_data[0].asnumpy()
 y = np.exp(x - x.max(axis=1).reshape((x.shape[0], 1)))
 y /= y.sum(axis=1).reshape((x.shape[0], 1))
 print self.xxx, self.yyy
 self.assign(out_data[0], req[0], mx.nd.array(y))

 def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
 l = in_data[1].asnumpy().ravel().astype(np.int)
 y = out_data[0].asnumpy()
 y[np.arange(l.shape[0]), l] -= 1.0
 self.assign(in_grad[0], req[0], mx.nd.array(y))

@mx.operator.register("softmax")
class SoftmaxProp(mx.operator.CustomOpProp):
 def __init__(self, xxx, yyy):
 super(SoftmaxProp, self).__init__(need_top_grad=False)

 # add parameter
 self.xxx = xxx
 self.yyy = yyy
 def list_arguments(self):
 return ['data', 'label', 'xxx', 'yyy']

 def list_outputs(self):
 return ['output']

 def infer_shape(self, in_shape):
 data_shape = in_shape[0]
 label_shape = (in_shape[0][0],)
 output_shape = in_shape[0]
 return [data_shape, label_shape], [output_shape], 

 def create_operator(self, ctx, shapes, dtypes):
 return Softmax(xxx=self.xxx, yyy=self.yyy)

Take a look at https://mxnet.incubator.apache.org/faq/new_op.html for full info.

Vishaal