result of ###Running the Model in Course 1 - Part 8 - Lesson 3 - Notebook

[mazzingkaizer]

Mr. Moroney, I always get a lot of help from your great courses. Thank you. ^^

The following is the result of ##Running the Model in Course 1 - Part 8 - Lesson 3 - which I modified and executed.
I modified the source code a little because I wanted to check the results on local PC.
The execution result is MISS CLASSIFICATION RATIO: 14.0625
This is the rate at which a horse image is judged to be human or a person image is judged to be a horse.
I think the rate of misjudgment is higher than expected.

How do I get this ratio close to zero percent?

------ modified source code "##Running the Model in Course 1 - Part 8 - Lesson 3 " -------
import numpy as np
#from google.colab import files
from tensorflow.keras.preprocessing import image

#uploaded = files.upload()
#for fn in uploaded.keys():

valid_image_list = [ { 'path': validation_horse_dir, 'images': os.listdir(validation_horse_dir) },
{ 'path': validation_human_dir, 'images': os.listdir(validation_human_dir) }]

total_image_cnt = len(os.listdir(validation_horse_dir)) + len(os.listdir(validation_human_dir))
miss_classification_count = 0
for valid_image in valid_image_list:
for image_name in valid_image['images']:
# predicting images
path = '{}/{}'.format(valid_image['path'], image_name)
#print("path:{}".format(path))
img = image.load_img(path, target_size=(300, 300))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)

    images = np.vstack([x])
    classes = model.predict(images, batch_size=10)
    #print(classes[0])
    if classes[0] > 0.5:
        if image_name.find("horse") > -1:
            miss_classification_count += 1
            res = "WRONG"
        else:
            res = "CORRECT"

        print("{} {} is a human : {}".format(classes[0], path, res))
    else:
        if image_name.find("human") > -1:
            miss_classification_count += 1
            res = "WRONG"
        else:
            res = "CORRECT"
        print("{} {} is a horse : {}".format(classes[0], path, res))

print("MISS CLASSIFICATION RATIO : {}".format((miss_classification_count / total_image_cnt) * 100))