EsraaRagaa opened a new issue #8562: Number of output from the mx.sym.SoftmaxOutput URL: https://github.com/apache/incubator-mxnet/issues/8562 ## I am training a model using CNN to classify images between class 1 and 0, but after prediction, I get number of classes equal to number of nodes in the fully connected layer which precedes the softmax layer # This is the code I use for building the model: data = mx.sym.var('data') conv1 = mx.sym.Convolution(data=data, kernel=(3,3), num_filter=6) relu1 = mx.sym.Activation(data=conv1, act_type="relu") pool1 = mx.sym.Pooling(data=relu1, pool_type="max", kernel=(2,2), stride=(2,2)) conv2 = mx.sym.Convolution(data=pool1, kernel=(6,6), num_filter=12) relu2 = mx.sym.Activation(data=conv2, act_type="relu") pool2 = mx.sym.Pooling(data=relu2, pool_type="max", kernel=(2,2), stride=(2,2)) flatten = mx.sym.flatten(data=pool2) fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=48) lenet = mx.sym.SoftmaxOutput(data=fc1,name='softmax') lenet_model = mx.mod.Module(symbol=lenet, context=mx.cpu()) lenet_model.fit(train_iterator, ..... num_epoch=10) # The solutions I tried to use to fix the problem: 1- lables = mx.sym.Variable('softmax_label') lenet = mx.sym.SoftmaxOutput(data=fc1,label=lables,name='softmax') * result: did not fix the problem, still after prediciton the number probability coresponding to the classes=48 2- lables = mx.sym.Variable('softmax_label') lenet = mx.sym.SoftmaxOutput(data=fc1,label=lables, preserve_shape=True, name='softmax') * result: did not fix the problem, still after prediciton the number probability coresponding to the classes=48 3- lables = mx.sym.Variable('softmax_label') lenet = mx.sym.SoftmaxOutput(data=fc1,label=lables, preserve_shape=True, multi_output=True, name='softmax') * result: did not fix the problem, still after prediciton the number probability coresponding to the classes=48 4- adding an extra hidden layer after fc1and pass it to the softmax extra_hidden_2nods = mx.symbol.FullyConnected(data=fc1, num_hidden=2) lenet = mx.sym.SoftmaxOutput(data=extra_hidden_2nods, name='softmax') * result: fixed the problem but this is not what I want ## Environment info (Required) I am using the last version of mxnet, python, anaconda Thanks in advance
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