Keras Binary Classification - Sigmoid activation function

Question

I've implemented a basic MLP in Keras with tensorflow and I'm trying to solve a binary classification problem. For binary classification, it seems that sigmoid is the recommended activation function and I'm not quite understanding why, and how Keras deals with this.

I understand the sigmoid function will produce values in a range between 0 and 1. My understanding is that for classification problems using sigmoid, there will be a certain threshold used to determine the class of an input (typically 0.5). In Keras, I'm not seeing any way to specify this threshold, so I assume it's done implicitly in the back-end? If this is the case, how is Keras distinguishing between the use of sigmoid in a binary classification problem, or a regression problem? With binary classification, we want a binary value, but with regression a nominal value is needed. All I can see that could be indicating this is the loss function. Is that informing Keras on how to handle the data?

Additionally, assuming Keras is implicitly applying a threshold, why does it output nominal values when I use my model to predict on new data?

For example:

y_pred = model.predict(x_test)
print(y_pred)

gives:

[7.4706882e-02] [8.3481872e-01] [2.9314638e-04] [5.2297767e-03] [2.1608515e-01] ... [4.4894204e-03] [5.1120580e-05] [7.0263929e-04]

I can apply a threshold myself when predicting to get a binary output, however surely Keras must be doing that anyway in order to correctly classify? Perhaps Keras is applying a threshold when training the model, but when I use it to predict new values, the threshold isn't used as the loss function isn't used in predicting? Or is not applying a threshold at all, and the nominal values outputted happen to be working well with my model? I've checked this is happening on the Keras example for binary classification, so I don't think I've made any errors with my code, especially as it's predicting accurately.

If anyone could explain how this is working, I would greatly appreciate it.

Here's my model as a point of reference:

model = Sequential()
model.add(Dense(124, activation='relu', input_shape = (2,)))
model.add(Dropout(0.5))
model.add(Dense(124, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(1, activation='sigmoid'))
model.summary()

model.compile(loss='binary_crossentropy',
              optimizer=SGD(lr = 0.1, momentum = 0.003),
              metrics=['acc'])

history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)

Answer 1

You can assign the threshold explicitly in compile() by using

tf.keras.metrics.BinaryAccuracy(
    name="binary_accuracy", dtype=None, threshold=0.5
)

like following:

model.compile(optimizer='sgd',
              loss='mse',
              metrics=[tf.keras.metrics.BinaryAccuracy()])

Answer 2

I did something similar recently for a students lesson. Here is an example:

https://tweeter-soup.herokuapp.com/

here is the repo for the three.js code:

https://github.com/soupIsTheCurrencyOfTheFuture/tweeter/blob/main/public/scripts/profile-icon.js

window.addEventListener('mousemove', (event) => {
  modelGroup.rotation.y = (event.clientX / window.innerWidth) - 0.5;
  modelGroup.rotation.x = (event.clientY / window.innerHeight) - 0.5;

  modelGroup.position.x = ((event.clientX / window.innerWidth) - 0.5) * 15;
  modelGroup.position.y = ((event.clientY / window.innerHeight) - 0.5) * -15;
});

here is the event listener.

you should be able to just change your current onclick event listener to a mousemove and you should be set though