Image Retrieval using Deep Features

In this article, we focused on using deep learning to create non-linear features to improve the performance of machine learning. We will also see how transfer learning techniques can be applied to use deep features learned with one dataset to get great performance on a different dataset. In this Ipython notebook, we are going to build new image retrieval models and explore their results on different parts of our image dataset

import graphlab

Load the CIFAR-10 dataset

# CSV format datasets https://d396qusza40orc.cloudfront.net/phoenixassets/image_train_data.csv
# https://d396qusza40orc.cloudfront.net/phoenixassets/image_test_data.csv
image_train = graphlab.SFrame('coursera-notebooks/course-1/image_train_data')
image_test =  graphlab.SFrame('coursera-notebooks/course-1/image_test_data')

image_train.head()
id image label deep_features image_array
24 Height: 32 Width: 32 bird [0.242871761322,
1.09545373917, 0.0, ...		 [73.0, 77.0, 58.0, 71.0,
68.0, 50.0, 77.0, 69.0, ...
33 Height: 32 Width: 32 cat [0.525087952614, 0.0,
0.0, 0.0, 0.0, 0.0, ...		 [7.0, 5.0, 8.0, 7.0, 5.0,
8.0, 5.0, 4.0, 6.0, 7.0, ...
36 Height: 32 Width: 32 cat [0.566015958786, 0.0,
0.0, 0.0, 0.0, 0.0, ...		 [169.0, 122.0, 65.0,
131.0, 108.0, 75.0, ...
70 Height: 32 Width: 32 dog [1.12979578972, 0.0, 0.0,
0.778194487095, 0.0, ...		 [154.0, 179.0, 152.0,
159.0, 183.0, 157.0, ...
90 Height: 32 Width: 32 bird [1.71786928177, 0.0, 0.0,
0.0, 0.0, 0.0, ...		 [216.0, 195.0, 180.0,
201.0, 178.0, 160.0, ...
97 Height: 32 Width: 32 automobile [1.57818555832, 0.0, 0.0,
0.0, 0.0, 0.0, ...		 [33.0, 44.0, 27.0, 29.0,
44.0, 31.0, 32.0, 45.0, ...
107 Height: 32 Width: 32 dog [0.0, 0.0,
0.220677852631, 0.0, ...		 [97.0, 51.0, 31.0, 104.0,
58.0, 38.0, 107.0, 61.0, ...
121 Height: 32 Width: 32 bird [0.0, 0.23753464222, 0.0,
0.0, 0.0, 0.0, ...		 [93.0, 96.0, 88.0, 102.0,
106.0, 97.0, 117.0, ...
136 Height: 32 Width: 32 automobile [0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 7.5737862587, 0.0, ...		 [35.0, 59.0, 53.0, 36.0,
56.0, 56.0, 42.0, 62.0, ...
138 Height: 32 Width: 32 bird [0.658935725689, 0.0,
0.0, 0.0, 0.0, 0.0, ...		 [205.0, 193.0, 195.0,
200.0, 187.0, 193.0, ...
[10 rows x 5 columns]

Train a nearest neighbour model for retrieving images using deep features

knn_model = graphlab.nearest_neighbors.create(image_train,
                                             features=['deep_features'],
                                             label='id')

PROGRESS: Starting brute force nearest neighbors model training.

Use image retrival model with deep features to find similar images

graphlab.canvas.set_target('ipynb')
cat = image_train[18:19]
cat['image'].show()

knn_model.query(cat)

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 7.002ms      |
PROGRESS: | Done         |         | 100         | 338.929ms    |
PROGRESS: +--------------+---------+-------------+--------------+
query_label reference_label distance rank
0 384 0.0 1
0 6910 36.9403137951 2
0 39777 38.4634888975 3
0 36870 39.7559623119 4
0 41734 39.7866014148 5
[5 rows x 4 columns]
def get_images_id(query_result):
    return image_train.filter_by(query_result['reference_label'],'id')

cat_neighbours = get_images_id(knn_model.query(cat))

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 8.562ms      |
PROGRESS: | Done         |         | 100         | 307.947ms    |
PROGRESS: +--------------+---------+-------------+--------------+

cat_neighbours['image'].show()

car = image_train[8:9]
car['image'].show()

get_images_id(knn_model.query(car))['image'].show()

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 7.241ms      |
PROGRESS: | Done         |         | 100         | 289.806ms    |
PROGRESS: +--------------+---------+-------------+--------------+

Just for fun, let's create a lambda function

show_neightbors = lambda i:get_images_id(knn_model.query(image_train[i:i+1]))['image'].show()

show_neightbors(8)

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 8.657ms      |
PROGRESS: | Done         |         | 100         | 303.641ms    |
PROGRESS: +--------------+---------+-------------+--------------+

show_neightbors(26)

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 6.87ms       |
PROGRESS: | Done         |         | 100         | 332.992ms    |
PROGRESS: +--------------+---------+-------------+--------------+

show_neightbors(1222)

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 6.621ms      |
PROGRESS: | Done         |         | 100         | 299.262ms    |
PROGRESS: +--------------+---------+-------------+--------------+

show_neightbors(2000)

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.0498753   | 6.76ms       |
PROGRESS: | Done         |         | 100         | 291.575ms    |
PROGRESS: +--------------+---------+-------------+--------------+

Creating category-specific image retrieval models

image_train_dog = image_train[image_train['label'] == 'dog']

len(image_train_dog)

509

image_train_cat = image_train[image_train['label'] == 'cat']

len(image_train_cat)

509

image_train_automobile = image_train[image_train['label'] == 'automobile']

len(image_train_automobile)

509

image_train_bird = image_train[image_train['label'] == 'bird']

len(image_train_bird)

478

image_train_automobile.head()
id image label deep_features image_array
97 Height: 32 Width: 32 automobile [1.57818555832, 0.0, 0.0,
0.0, 0.0, 0.0, ...		 [33.0, 44.0, 27.0, 29.0,
44.0, 31.0, 32.0, 45.0, ...
136 Height: 32 Width: 32 automobile [0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 7.5737862587, 0.0, ...		 [35.0, 59.0, 53.0, 36.0,
56.0, 56.0, 42.0, 62.0, ...
302 Height: 32 Width: 32 automobile [0.583938002586, 0.0,
0.0, 0.0, 0.0, 0.0, ...		 [64.0, 52.0, 37.0, 85.0,
60.0, 40.0, 92.0, 66.0, ...
312 Height: 32 Width: 32 automobile [0.0, 0.0, 0.0,
0.392823398113, 0.0, ...		 [124.0, 126.0, 113.0,
124.0, 126.0, 113.0, ...
323 Height: 32 Width: 32 automobile [0.0, 0.0, 0.0,
4.42310428619, ...		 [241.0, 241.0, 241.0,
238.0, 238.0, 238.0, ...
536 Height: 32 Width: 32 automobile [0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 8.42903900146, 0.0, ...		 [164.0, 154.0, 154.0,
128.0, 119.0, 120.0, ...
593 Height: 32 Width: 32 automobile [1.65033948421, 0.0, 0.0,
0.0, 0.0, 0.0, ...		 [231.0, 222.0, 227.0,
232.0, 217.0, 221.0, ...
962 Height: 32 Width: 32 automobile [0.0, 0.0, 0.0,
0.39552795887, 0.0, 0.0, ...		 [255.0, 255.0, 255.0,
255.0, 255.0, 255.0, ...
997 Height: 32 Width: 32 automobile [0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 8.04085636139, 0.0, ...		 [145.0, 148.0, 157.0,
131.0, 134.0, 145.0, ...
1421 Height: 32 Width: 32 automobile [0.0, 0.0, 0.0, 0.0, 0.0,
0.359612941742, ...		 [114.0, 95.0, 33.0,
118.0, 98.0, 26.0, 91.0, ...
[10 rows x 5 columns]
dog_knn_model = graphlab.nearest_neighbors.create(image_train_dog,
                                             features=['deep_features'],
                                             label='id')

PROGRESS: Starting brute force nearest neighbors model training.

cat_knn_model = graphlab.nearest_neighbors.create(image_train_cat,
                                             features=['deep_features'],
                                             label='id')

PROGRESS: Starting brute force nearest neighbors model training.

automobile_knn_model = graphlab.nearest_neighbors.create(image_train_automobile,
                                             features=['deep_features'],
                                             label='id')

PROGRESS: Starting brute force nearest neighbors model training.

bird_knn_model = graphlab.nearest_neighbors.create(image_train_bird,
                                             features=['deep_features'],
                                             label='id')

PROGRESS: Starting brute force nearest neighbors model training.

image_test[0:1]
id image label deep_features image_array
0 Height: 32 Width: 32 cat [1.13469004631, 0.0, 0.0,
0.0, 0.0366497635841, ...		 [158.0, 112.0, 49.0,
159.0, 111.0, 47.0, ...
[1 rows x 5 columns]
image_test[0:1]['image'].show()

test_cat = image_test[0:1]

cat_knn_model.query(test_cat)

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.196464    | 7.432ms      |
PROGRESS: | Done         |         | 100         | 80.534ms     |
PROGRESS: +--------------+---------+-------------+--------------+
query_label reference_label distance rank
0 16289 34.623719208 1
0 45646 36.0068799284 2
0 32139 36.5200813436 3
0 25713 36.7548502521 4
0 331 36.8731228168 5
[5 rows x 4 columns]
def get_images_id_cat(query_result):
    return image_train_cat.filter_by(query_result['reference_label'],'id')

get_images_id_cat(cat_knn_model.query(test_cat))['image'].show()

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.196464    | 7.881ms      |
PROGRESS: | Done         |         | 100         | 83.519ms     |
PROGRESS: +--------------+---------+-------------+--------------+

test_cat_neighbours = cat_knn_model.query(test_cat)

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.196464    | 6.998ms      |
PROGRESS: | Done         |         | 100         | 82.467ms     |
PROGRESS: +--------------+---------+-------------+--------------+

test_cat_neighbours
query_label reference_label distance rank
0 16289 34.623719208 1
0 45646 36.0068799284 2
0 32139 36.5200813436 3
0 25713 36.7548502521 4
0 331 36.8731228168 5
[5 rows x 4 columns]
image_train_cat.filter_by(test_cat_neighbours['reference_label'],'id').[1]
id image label deep_features image_array
331 Height: 32 Width: 32 cat [0.0, 0.0,
0.510963916779, 0.0, ...		 [45.0, 65.0, 92.0, 72.0,
95.0, 110.0, 106.0, ...
16289 Height: 32 Width: 32 cat [0.964287519455, 0.0,
0.0, 0.0, 1.12515509129, ...		 [215.0, 219.0, 231.0,
215.0, 219.0, 232.0, ...
25713 Height: 32 Width: 32 cat [0.536971271038, 0.0,
0.0, 0.0894458889961, ...		 [228.0, 222.0, 236.0,
224.0, 213.0, 222.0, ...
32139 Height: 32 Width: 32 cat [1.29409468174, 0.0, 0.0,
0.513800263405, ...		 [217.0, 220.0, 205.0,
221.0, 227.0, 218.0, ...
45646 Height: 32 Width: 32 cat [0.983677506447, 0.0,
0.0, 0.0, 0.0, ...		 [51.0, 42.0, 26.0, 56.0,
47.0, 31.0, 59.0, 50.0, ...
[5 rows x 5 columns]
nearest_cat_sframe = image_train_cat.filter_by(test_cat_neighbours['reference_label'],'id')

nearest_cat_sframe['image'].show()

get_images_id(dog_knn_model.query(test_cat))['image'].show()

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.196464    | 6.663ms      |
PROGRESS: | Done         |         | 100         | 79.548ms     |
PROGRESS: +--------------+---------+-------------+--------------+

nearest_dog = dog_knn_model.query(test_cat)

PROGRESS: Starting pairwise querying.
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 0            | 1       | 0.196464    | 7.628ms      |
PROGRESS: | Done         |         | 100         | 84.028ms     |
PROGRESS: +--------------+---------+-------------+--------------+

nearest_dog
query_label reference_label distance rank
0 16976 37.4642628784 1
0 13387 37.5666832169 2
0 35867 37.6047267079 3
0 44603 37.7065585153 4
0 6094 38.5113254907 5
[5 rows x 4 columns]
image_train_dog.filter_by(nearest_dog['reference_label'],'id')
id image label deep_features image_array
6094 Height: 32 Width: 32 dog [0.470533549786, 0.0,
0.0, 0.0, 0.0, 0.0, ...		 [91.0, 98.0, 71.0, 138.0,
123.0, 63.0, 135.0, ...
13387 Height: 32 Width: 32 dog [0.366494178772, 0.0,
0.0, 0.0, 0.0, 0.0, ...		 [255.0, 255.0, 255.0,
255.0, 255.0, 255.0, ...
16976 Height: 32 Width: 32 dog [0.755595386028, 0.0,
0.0, 0.0, 0.0, 0.0, ...		 [16.0, 17.0, 11.0, 18.0,
19.0, 13.0, 20.0, 21.0, ...
35867 Height: 32 Width: 32 dog [0.305321395397, 0.0,
0.0, 0.0, 0.0, 0.0, ...		 [101.0, 93.0, 9.0, 93.0,
88.0, 9.0, 90.0, 86.0, ...
44603 Height: 32 Width: 32 dog [0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 11.2646656036, 0.0, ...		 [8.0, 25.0, 9.0, 29.0,
39.0, 22.0, 66.0, 75.0, ...
[5 rows x 5 columns]
image_train_dog.filter_by(nearest_dog['reference_label'],'id')['image'].show()

calculating mean distances from neighbours

test_cat_neighbours
query_label reference_label distance rank
0 16289 34.623719208 1
0 45646 36.0068799284 2
0 32139 36.5200813436 3
0 25713 36.7548502521 4
0 331 36.8731228168 5
[5 rows x 4 columns]
test_cat_neighbours['distance'].show()

nearest_dog
query_label reference_label distance rank
0 16976 37.4642628784 1
0 13387 37.5666832169 2
0 35867 37.6047267079 3
0 44603 37.7065585153 4
0 6094 38.5113254907 5
[5 rows x 4 columns]
nearest_dog['distance'].show()

Computing nearest neighbors accuracy

len(image_test)

4000

image_test['label'].sketch_summary()

+------------------+-------+----------+
|       item       | value | is exact |
+------------------+-------+----------+
|      Length      |  4000 |   Yes    |
| # Missing Values |   0   |   Yes    |
| # unique values  |   4   |    No    |
+------------------+-------+----------+

Most frequent items:
+-------+------------+------+------+------+
| value | automobile | cat  | bird | dog  |
+-------+------------+------+------+------+
| count |    1000    | 1000 | 1000 | 1000 |
+-------+------------+------+------+------+

image_test_cat = image_test[image_test['label'] == 'cat']

len(image_test_cat)

1000

image_test_dog = image_test[image_test['label'] == 'dog']

image_test_automobile = image_test[image_test['label'] == 'automobile']

image_test_bird = image_test[image_test['label'] == 'bird']

dog_cat_neighbors = cat_knn_model.query(image_test_dog, k=1)

PROGRESS: Starting blockwise querying.
PROGRESS: max rows per data block: 7668
PROGRESS: number of reference data blocks: 1
PROGRESS: number of query data blocks: 1
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 1000         | 509000  | 100         | 791.418ms    |
PROGRESS: | Done         | 509000  | 100         | 794.145ms    |
PROGRESS: +--------------+---------+-------------+--------------+

dog_cat_neighbors
query_label reference_label distance rank
0 33 36.4196077068 1
1 30606 38.8353268874 1
2 5545 36.9763410854 1
3 19631 34.5750072914 1
4 7493 34.778824791 1
5 47044 35.1171578292 1
6 13918 40.6095830913 1
7 10981 39.9036867306 1
8 45456 38.0674700168 1
9 44673 42.7258732951 1
[1000 rows x 4 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns. 
dog_dog_neighbors = dog_knn_model.query(image_test_dog, k=1)

PROGRESS: Starting blockwise querying.
PROGRESS: max rows per data block: 7668
PROGRESS: number of reference data blocks: 1
PROGRESS: number of query data blocks: 1
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 1000         | 509000  | 100         | 790.626ms    |
PROGRESS: | Done         | 509000  | 100         | 793.114ms    |
PROGRESS: +--------------+---------+-------------+--------------+

dog_dog_neighbors
query_label reference_label distance rank
0 49803 33.4773590373 1
1 5755 32.8458495684 1
2 20715 35.0397073189 1
3 13387 33.9010327697 1
4 12089 37.4849250909 1
5 6094 34.945165344 1
6 3431 39.0957278345 1
7 6184 37.7696131032 1
8 2167 35.1089144603 1
9 7776 43.2422832585 1
[1000 rows x 4 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns. 
dog_automobile_neighbors = automobile_knn_model.query(image_test_dog, k=1)

PROGRESS: Starting blockwise querying.
PROGRESS: max rows per data block: 7668
PROGRESS: number of reference data blocks: 1
PROGRESS: number of query data blocks: 1
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 1000         | 509000  | 100         | 797.994ms    |
PROGRESS: | Done         | 509000  | 100         | 800.718ms    |
PROGRESS: +--------------+---------+-------------+--------------+

dog_automobile_neighbors
query_label reference_label distance rank
0 33859 41.9579761457 1
1 2046 46.0021331807 1
2 19594 42.9462290692 1
3 11000 41.6866060048 1
4 19594 39.2269664935 1
5 49314 40.5845117698 1
6 40822 45.1067352961 1
7 44997 41.3221140974 1
8 33859 41.8244654995 1
9 33859 45.4976929401 1
[1000 rows x 4 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns. 
dog_bird_neighbors = bird_knn_model.query(image_test_dog, k=1)

PROGRESS: Starting blockwise querying.
PROGRESS: max rows per data block: 7668
PROGRESS: number of reference data blocks: 1
PROGRESS: number of query data blocks: 1
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |
PROGRESS: +--------------+---------+-------------+--------------+
PROGRESS: | 1000         | 478000  | 100         | 765.493ms    |
PROGRESS: | Done         | 478000  | 100         | 768.23ms     |
PROGRESS: +--------------+---------+-------------+--------------+

dog_bird_neighbors
query_label reference_label distance rank
0 44658 41.7538647304 1
1 9215 41.3382958925 1
2 36675 38.6157590853 1
3 12582 37.0892269954 1
4 36122 38.272288694 1
5 8736 39.1462089236 1
6 38991 40.523040106 1
7 44177 38.1947918393 1
8 4549 40.1567131661 1
9 40225 45.5597962603 1
[1000 rows x 4 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns. 
dog_distances = graphlab.SFrame({'dog-dog': dog_dog_neighbors['distance'],'dog-cat': dog_cat_neighbors['distance'], 
                                'dog-automobile': dog_automobile_neighbors['distance'], 'dog-bird': dog_bird_neighbors['distance']})

dog_distances
dog-automobile dog-bird dog-cat dog-dog
41.9579761457 41.7538647304 36.4196077068 33.4773590373
46.0021331807 41.3382958925 38.8353268874 32.8458495684
42.9462290692 38.6157590853 36.9763410854 35.0397073189
41.6866060048 37.0892269954 34.5750072914 33.9010327697
39.2269664935 38.272288694 34.778824791 37.4849250909
40.5845117698 39.1462089236 35.1171578292 34.945165344
45.1067352961 40.523040106 40.6095830913 39.0957278345
41.3221140974 38.1947918393 39.9036867306 37.7696131032
41.8244654995 40.1567131661 38.0674700168 35.1089144603
45.4976929401 45.5597962603 42.7258732951 43.2422832585
[1000 rows x 4 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns. 
dog_distances[1]

{'dog-automobile': 46.002133180677895,
 'dog-bird': 41.3382958924861,
 'dog-cat': 38.83532688735544,
 'dog-dog': 32.845849568405555}

def is_dog_correct(row):
    if ( min(row, key=row.get) == 'dog-dog'):
        return 1
    else:
        return 0

is_dog_correct(dog_distances[1])

1

is_dog_correct(dog_distances[9])

0

dog_distances.apply(is_dog_correct)

dtype: int
Rows: 1000
[1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, ... ]

dog_distances.apply(is_dog_correct).sum()

678

accuracy = dog_distances.apply(is_dog_correct).sum() / float (1000)

accuracy

0.678



Credits Machine Learning Foundations: A Case Study Approach