class activation mapping when accuracy is 100%

I am a beginner to image classification and apologies beforehand if the question I am asking is dumb. I am currently using the following model:

baseModel = VGG16(weights="imagenet", include_top=False,
input_tensor=Input(shape=(224, 224, 3)))

# construct the head of the model that will be placed on top of the base model


For this model, I am doing a binary image classification. The model is compiled as such:

opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="binary_crossentropy", optimizer=opt,
metrics=["accuracy"])


This model will give a 100% for both the training and validation accuracy. I wanted to understand how the model is activated for certain images. So I tried to calculating the heatmap. However in one of the calculations we have:

    def compute_heatmap(self, image, eps=1e-8):

inputs=[self.model.inputs],
outputs=[self.model.get_layer(self.layerName).output,
self.model.output])