currency_prediction.py

# pick random test data sample from one batch
x = random.randint(0, 41) # test set has 42 samples

for i in test_ds.as_numpy_iterator():
    img, label = i    
    plt.axis('off')   # remove axes
    #print(img.shape, x)
    plt.imshow(img[x])    # shape from (64, 256, 256, 3) --> (256, 256, 3)
    output = model.predict(np.expand_dims(img[x],0))    # getting output; input shape (256, 256, 3) --> (1, 256, 256, 3)
    pred = np.argmax(output[0])    # finding max
    print("Prdicted: ", class_names[pred])    # Picking the label from class_names base don the model output
    print("True: ", class_names[np.argmax(label[x])])
    print("Probability: ", output[0][pred])
    break