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I probably don't understand the whole context but I see there could be a couple problems: You want to use pretrained model by classification on ImageNet for face recognition task. Those are very different task and the representation from pretrained ResNet will not be very useful for face recognition. You have just 3 classes. Do you mean just 3 person? That ...


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Another benefit of a convex loss function is it will have faster convergence for all models, both linear and nonlinear. There will be even faster convergence in a convex loss function if a momentum term is added to gradient descent. However often in real-world scenarios and with many models types, the loss function is not guaranteed to be convex. It is not ...


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You can make a model with multiple (two) different outputs, the first output should predict your most important label, and the second dense layer ( in Keras) should predict others. something like below code in last layers: outputs1 = keras.layers.Dense(1, activation='sigmoid', name='loss1')(x) outputs2 = keras.layers.Dense(13, activation='...


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You might be winding up on the other side of the classification threshold. Remember that the neural network returns probabilities, not categories. If your predicted probability moves from $0.51$ in one model to $0.49$ in the other, that is a small change, but if you set the threshold for classification at $0.5$, the models give different categories. If you ...


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