Understanding Tensorflow Binary Image Classification Results

For one of my first attempts at using Tensor flow I've followed the Binary Image Classification tutorial https://www.tensorflow.org/tutorials/keras/text_classification_with_hub#eva

Solution 1:

According to the link that you provided, the problem come from your output activation function. That code use dense vector with 1 neuron without activation function. So it just multiplying output from previous layer with weight and bias and sum them together. The output that you get will have a range between -infinity(negative class) and +infinity(positive class), Therefore if you really want your output between zero and one you need an activation function such as sigmoid model.add(tf.keras.layers.Dense(1), activation='sigmoid'). Now we just map every thing to range 0 to 1, so we can classify as negative class if output is less than 0.5(mid point) and vice versa.

Actually your understanding of prediction function is correct. You simply did not add an activation to fit with your assumption, that's why you gat that output instead of value between 0 and 1.