tz-hmc commented on issue #8591: How do I make a siamese network with pretrained models (esp. keeping the weights the same?) URL: https://github.com/apache/incubator-mxnet/issues/8591#issuecomment-343367913 Hey again, I tried something like this, but I still have a lot of questions: ``` sym1, arg_params, aux_params = get_model() sym2, arg_params, aux_params = get_model() mod1 = mx.mod.Module(symbol=sym1, context=mx.cpu(), label_names=None) mod2 = mx.mod.Module(symbol=sym2, context=mx.cpu(), label_names=None) mod1.bind(for_training=True, shared_module=mod2, data_shapes=[('data', (1,3,224,224))], # true to train label_shapes=mod1._label_shapes) mod2.bind(for_training=True, shared_module=mod1, data_shapes=[('data', (1,3,224,224))], # true to train label_shapes=mod2._label_shapes) mod1.set_params(arg_params, aux_params, allow_missing=True) mod2.set_params(arg_params, aux_params, allow_missing=True) out1 = sym1.get_internals()['flatten0_output'] out2 = sym2.get_internals()['flatten0_output'] siamese_out = mx.sym.Concat(out1, out2, dim=0) # Stacked network after it fc1 = mx.symbol.FullyConnected(data = siamese_out, name='fc1', num_hidden=128) act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu") fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64) act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu") fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=num_classes) mlp = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax') # new_args = dict() mod3 = mx.mod.Module(fc1, context=mx.cpu(), label_names=None) mod3 = fe_mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))]) mod3.set_params(arg_params, aux_params) ``` I only want the first part of this network (layers attached to mod2 & mod1) to be shared. Would something like this work & still backpropagate errors appropriately when fitted? Having to run mod.fit on each part of the network could be inconvenient. Is there a way around this?
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