Making predictions

First, let's prepare data to make predictions about the images:

array = np.mat(data.pixels[1]).reshape(48, 48) 
image = scipy.misc.toimage(array, cmin=0.0) 
display(image) 
print(emotion_labels[data.emotion[1]]) 
 
<Image> 
 

Let us input an angry emotion image:

input_img = np.array(array).reshape(1,48,48,1)

Okay, we have an angry face. Now let's make prediction and check if the network can recognize it correctly:

prediction = model.predict(input_img) 
print(prediction) 
[[ 0.05708674  0.35863262  0.03299783  0.17862292  0.00069717  0.37196276]] 
emotion_labels[prediction.argmax()] 
'Neutral' 

Note those array of 6 float numbers. These are probabilities of belonging to each class. In other words, the model predicts, that this face can be of an angry person only with the probability of 5%. The full table would look like this:

Angry...