Machine Learning w/ Python: Project 2 (Dog, Cat images) - probabilities list question


I am trying to complete project 2 of the Machine Learning with Python certification, and am stuck on trying to figure out how I can get “probabilities” for test data?

In the code below, what is the correct way of calling plotImages(), with the probabilities list?

def plotImages(images_arr, probabilities = False):

    fig, axes = plt.subplots(len(images_arr), 1, figsize=(5,len(images_arr) * 3))

    if probabilities is False:

      for img, ax in zip( images_arr, axes):




      for img, probability, ax in zip( images_arr, probabilities, axes):



          if probability > 0.5:

              ax.set_title("%.2f" % (probability*100) + "% dog")


              ax.set_title("%.2f" % ((1-probability)*100) + "% cat")

sample_training_images, _ = next(train_data_gen)


Link to my colab workbook:


At the moment, your accuracy is 50% on both training and validation. Change your final layer to have two nodes, rather than just one.


You can get the probabilities variable for the final test function like so

predicted_class = model.predict_classes(test_data_gen)
probabilities = predicted_class.tolist()

This is sufficient for you to pass the test function at the end. If you want the actual probability though, you can use something like this

# gives you the probability of cat in one column and dog in the other
result = model.predict(test_data_gen) 
# extract the probability corresponding to the predicted class
actual_probabilities = [np.max(vector) for vector in result]
1 Like

Awesome, thank you for going through my post! I was able to use your suggestion and plot compare the actual vs. expected.

In addition to the add(Dense(2)), I made a couple of other changes to the model, and was able to get accuracy over 70% (around 74%+).

As you were working through this exercise, how did you develop intuition for number of filters and kernel_size (for example in Conv2D()). I found myself trying one combination, then another, until I had a model that was accurate enough. Perhaps I need to read the documentation once more to develop better understanding.

Link to my updated notebook:

I don’t know if there is a definitive answer (or know much about this topic – I should read up on this too). I just played around with the number of neurons and the number of layers.

You want to have enough neurons to describe your problem but not too many for the model to overspecialise (as you want it to describe a general picture). So you probably want the minimum number that describes the complexity of the system with maximal accuracy. Beyond trial and error, I don’t know what the most sensible values should be.