5a3f444696ba821ce35e165747cd03c6e03b6c5a,art/estimators/embedding/keras.py,KerasAdversarialEmbedding,__init__,#KerasAdversarialEmbedding#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,54

Before Change

        losses = {"output_" + str(i + 1): loss for i, loss in model.outputs}
        losses["backdoor_detect"] = "binary_crossentropy"
        // losses = {"output_1": model.losses[0], "output_2": "binary_crossentropy"}
        print(self.embed_model.summary())
        self.embed_model.compile(loss=losses, loss_weights=[1.0] * len(self.model.loss_weights_list) +
                                                           [self.regularization])

After Change

        self.embed_model = Model(inputs=self.model.inputs, outputs=self.model.outputs + [backdoor_detect])
        // print("printing model summary")
        // Assuming outputs are default named output_1, ... output_n
        if not model.losses:
            // Assuming output layer is last layer
            output_layer = len(model.layers) - 1
            losses = {model.layers[output_layer].name: model.loss, "backdoor_detect": "binary_crossentropy"}
        else:
            // TODO: this makes no sense
            losses = {"output_" + str(i + 1): loss for i, loss in model.outputs}
            losses["backdoor_detect"] = "binary_crossentropy"
        // losses = {"output_1": model.losses[0], "output_2": "binary_crossentropy"}
        // print(self.embed_model.summary())
        // TODO: dynamically set optimizer and metric from original model
        self.embed_model.compile(optimizer="adam", loss=losses, loss_weights=[1.0] * len(self.model.loss_weights_list) +
                                                                             [-self.regularization],
                                 metrics=["accuracy"])
        print("model loss")

Instances