smpawlowski opened a new issue #16093: mx.viz.print_summary doesn't report parameters for a symbol URL: https://github.com/apache/incubator-mxnet/issues/16093 ## Description mx.viz.print_summary reports zero parameters for a symbol that has parameters. ## Environment info (Required) Windows 10, python, mxnet 1.5.0 ## Error Message: See example below that trains a network with 3 parameters. The parameters are correctly trained, but not reported by mx.viz.print_summary Output of the sample script below: Params before training: ------------------------------- ({'x': [0.5 0.5 0.5] <NDArray 3 @cpu(0)>}, {}) Params after training: -------------------------------- ({'x': [ 1.5396947e-05 -1.8216294e-08 2.7031724e-08] <NDArray 3 @cpu(0)>}, {}) mx.viz.print_summary reports Total params:0 ## Minimum reproducible example ``` import mxnet as mx ctx = mx.cpu(0) n = 100 num_features = 3 num_labels = 1 inputs: mx.sym.Symbol = mx.sym.var('data') x: mx.sym.Symbol = mx.sym.var('x', init=mx.init.Constant(value=0.5), shape=(num_features,)) out: mx.sym.Symbol = mx.sym.broadcast_mul(inputs, x) loss = mx.sym.LinearRegressionOutput(data=mx.sym.sum(mx.sym.abs(out), 1), label=mx.sym.var('softmax_label')) nd_iter = mx.io.NDArrayIter(data=mx.nd.random.normal(shape=(n, num_features)), label=mx.nd.zeros(shape=(n,)), batch_size=10) mod = mx.mod.Module(loss) mod.bind(data_shapes=nd_iter.provide_data, label_shapes=nd_iter.provide_label) mod.init_params() print('Params before training: -------------------------------') print(mod.get_params()) mod.fit(train_data=nd_iter, num_epoch=100, force_init=False) print('Params after training: --------------------------------') print(mod.get_params()) mx.viz.print_summary(symbol=out) ``` ## Steps to reproduce Run script above
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