If my model is trained using sigmoid at the final layer and binary_crossentropy, can I shtill out put probability of classes rather than 0/1?

Question

I have trained a CNN model with dense layer at the end using a sigmoid function:

model.add(layers.Dense(1, activation='sigmoid'))

I have also compiled using binary cross entropy:

model.compile(loss='binary_crossentropy',
              optimizer = 'Adam',
              metrics=[tf.keras.metrics.Precision(),tf.keras.metrics.Recall(),'accuracy'])

The f1 score of the binary images classification comes low and my model predicts one class over the other. So I decided to add a threshold based on the output probability of my sigmoid function at the final layer:

c = load_img('/home/kenan/Desktop/COV19D/validation/covid/ct_scan_19/120.jpg', 
             color_mode='grayscale',
             target_size = (512,512))
c=img_to_array(c)
c= np.expand_dims(c, axis=0)
pred = model.predict_proba(c)
pred
y_classes = ((model.predict(c)> 0.99)+0).ravel() 
y_classes

I want to use 'pred' in my code as a probability of the class but it is always either 0 or 1 as shown below:

Out[113]: array([[1.]], dtype=float32)

why doesn't it give the probability of predicting the class between [0,1] instead of 1? is there a way to get the class probability in my case rather than 0 or 1?

Answer 1

No you cant. Sigmoid activation in the final layer will output ONE value in the range of 0 to 1. If you want to obtain class probabilities of the different labels, you'll have to change the final layer activation to softmax.