Basic classification in tensorflow tutorial - python-3.x

Question

I just started learning tensorflow and am working on the basic classification tutorial in their official page.
Basic Classification Tutorial
From the below piece of code
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = 'blue'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array[i], true_label[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')
The below are the sample results of the test data.
Case 1:
Case 2
Even though the system predicted it with 100%, then why did it displayed the results with the red color?
In the predicted label, it is not showing any other classes.

Answer 1

There is a bug in the code.
"i" will for predictions_array, take the first array element(which is an array). This part is ok, but it is always going to be index 0 you need for the prediction array. Two ways to fix this: either pass it as "predictions_array[0]" when calling like I did below. Or modify the function to include "predictions_array = predictions_array[0]"
Since "i" must be 0 for the predictions array it will in the original code always check test_labels[0]. This will then for all cases when you predict something else than 9 give a red (since it thinks its a wrong prediction). Therefore passing i as the index of the test image will give you the correct label.
Suggestion of a modified function:
def plot_value_array(i, predictions_array, true_label):
print(true_label)
true_label = true_label[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')
And modified calling where "1" is the image I am in this case testing (make this a variable so you dont have to type it twice when testing).
In other words: in case img = test_images[1] I have to pass 1 to the function.
plot_value_array(1, predictions_single[0], test_labels)
plt.xticks(range(10), class_names, rotation=45)
plt.show()