Scaling the sigmoid output

I am training a Network on images for binary classification. The input images are normalized to have pixel values in the range[0,1]. Also, the weight matrices are initialized from a normal distribution. However, the output from my last Dense layer with sigmoid activation yields values with a very minute difference for the two classes. For example -

output for class1- 0.377525 output for class2- 0.377539

The difference for the classes comes after 4 decimal places. Is there any workaround to make sure that the output for class 1 falls around 0 to 0.5 and for class 2 , it falls between 0.5 to 1.

Edit:

I have tried both the cases.

Case 1 - Dense(1, 'sigmoid') with binary crossentropy Case 2- Dense(2, 'softmax') with binary crossentropy

For case1, the output values differ by a very small amount as mentioned in the problem above. As such , i am taking mean of the predicted values to act as threshold for classification. This works upto some extent, but not a permanent solution.

For case 2 - the prediction overfits to one class only.

A sample code : -

inputs = Input(shape = (128,156,1))
x = Conv2D(.....)(inputs)
x = BatchNormalization()(x)
x = Maxpooling2D()(x)
...
.
.
flat=Flatten()(x)

out = Dense(1,'sigmoid')(x)
model = Model(inputs,out)
model.compile(optimizer='adamax',loss='binary_crossentropy',metrics=['binary_accuracy'])

It seems you are confusing a binary classification architecture with a 2 label multi-class classification architecture setup.

Since you mention the probabilities for the 2 classes, class1 and class2, you have, set up a single label multi-class setup. That means, you are trying to predict the probabilities of 2 classes, where a sample can have only one of the labels at a time.

In this setup, it's proper to use softmax instead of sigmoid. Your loss function would be binary_crossentropy as well.

Right now, with the multi-label setup and sigmoid activation, you are independently predicting the probability of a sample being class1 and class2 simultaneously (aka, multi-label multi-class classification).

Once you change to softmax you should see more significant differences between the probabilities IF the sample actually definitively belongs to one of the 2 classes and if your model is well trained & confident about its predictions (validation vs training results)