liumilan commented on issue #8500: program crash when run sparse model predict URL: https://github.com/apache/incubator-mxnet/issues/8500#issuecomment-343683671 Linear_classfication.py is also changed # model # The positive class weight, says how much more we should upweight the importance of # positive instances in the objective function. # This is used to combat the extreme class imbalance. positive_class_weight = 2 model = linear_model(num_features, positive_class_weight) # module mod = mx.mod.Module(symbol=model,data_names=['data'], label_names=['softmax_label'],context=devs) mod.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label) mod.init_params() optim = mx.optimizer.create(optimizer, learning_rate=0.7,momentum = 0.8,rescale_grad=1.0/batch_size/num_worker) mod.init_optimizer(optimizer=optim, kvstore=kv) # use accuracy as the metric metric = mx.metric.create(['ce','auc']) # get the sparse weight parameter weight_index = mod._exec_group.param_names.index('weight') weight_param = mod._exec_group.param_arrays[weight_index] speedometer = mx.callback.Speedometer(batch_size, 100) mod.fit(train_data = train_data, eval_data = eval_data, eval_metric = metric, optimizer = optim, kvstore = kvstore, num_epoch = args.num_epoch, #batch_end_callback = mx.callback.Speedometer(batch_size, 10000), batch_end_callback = None, epoch_end_callback = save_model())
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