Probability for Tensorflow Binary Image Classification

Question

I try to follow the Image Classification Tutorial but unfortunally it doesn't tell you how to use the model after you've created it.

The code I currently use to create the model is:

model = Sequential([
    tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE ,3)),
    tf.keras.layers.MaxPooling2D(),
    tf.keras.layers.Dropout(0.1),
    tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
    tf.keras.layers.MaxPooling2D(),
    tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
    tf.keras.layers.MaxPooling2D(),
    tf.keras.layers.Dropout(0.1),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=['accuracy'])

On my first attempt I hadn't the activation='sigmoid' on the last Dense layer, but then the predictions I get from the model are for example [[332.9539]] which I don't know how to interpret.

After I read this answer I added the Sigmoid activation to receive a value between 0 and 1, but unfortunally when training the model the accuracy is stuck at 0.5 while it worked before.

What am I doing wrong?

Answer 1

If you add the sigmoid activation to the last layer, then you need to remove the from_logits=True from the loss instance, since your model is no longer producing logits:

model.compile(optimizer='adam',
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=['accuracy'])