understanding tensorflow binary image classification results

Question

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#evaluate_the_model .

I was able to follow the tutorial fine, but then I wanted to try to inspect the results more closely, namely I wanted to see what predictions the model made for each item in the test data set.

In short, I wanted to see what "label" (1 or 0) it would predict applies to a given movie review.

So I tried:

results = model.predict(test_data.batch(512))

and then

for i in results: print(i)

This gives me close to what I would expect. A list of 25,000 entries (one for each movie review).

But the value of each item in the array is not what I would expect. I was expecting to see a predicted label, so either a 0 (for negative) or 1 (for positive).

But instead I get this:

[0.22731477]
[2.1199656]
[-2.2581818]
[-2.7382329]
[3.8788114]
[4.6112833]
[6.125982]
[5.100685]
[1.1270659]
[1.3210837]
[-5.2568426]
[-2.9904163]
[0.17620209]
[-1.1293088]
[2.8757455]
...and so on for 25,000 entries.

Can someone help me understand what these numbers mean.

Am I misunderstanding what the "predict" method does, or (since these number look similar to the word embedding vectors introduced in the first layer of the model) perhaps I am misunderstanding how the prediction relates to the word embedding layer and the ultimate classification label.

I know this a major newbie question. But appreciate your help and patience:)

Answer 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.