Your First AI application

Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.

In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using this dataset from Oxford of 102 flower categories, you can see a few examples below.

The project is broken down into multiple steps:

  • Load the image dataset and create a pipeline.
  • Build and Train an image classifier on this dataset.
  • Use your trained model to perform inference on flower images.

We'll lead you through each part which you'll implement in Python.

When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.

Import Resources

In [4]:
# # Remove if Dataset Downloads need to be re-initiated due to CheckSum Error.
# !rm -rf ~/tensorflow_datasets/oxford_flowers102/
# !rm -rf ~/tensorflow_datasets/downloads

# The new version of dataset is only available in the tfds-nightly package.
%pip --no-cache-dir install tfds-nightly --user
Collecting tfds-nightly
  Downloading https://files.pythonhosted.org/packages/3d/db/c96e31b6893e9d760cd6658366598de6c62fc1ac6766773e049f3e85f0ca/tfds_nightly-3.1.0.dev202006280105-py3-none-any.whl (3.4MB)
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Note: you may need to restart the kernel to use updated packages.
In [1]:
# Imports
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_hub as hub
In [2]:
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image

Load the Dataset

Here you'll use tensorflow_datasets to load the Oxford Flowers 102 dataset. This dataset has 3 splits: 'train', 'test', and 'validation'. You'll also need to make sure the training data is normalized and resized to 224x224 pixels as required by the pre-trained networks.

The validation and testing sets are used to measure the model's performance on data it hasn't seen yet, but you'll still need to normalize and resize the images to the appropriate size.

In [3]:
# Download data to default local directory "~/tensorflow_datasets"
!python -m tensorflow_datasets.scripts.download_and_prepare --register_checksums=True --datasets=oxford_flowers102
I0628 23:59:28.305042 139695953327872 download_and_prepare.py:201] Running download_and_prepare for dataset(s):
oxford_flowers102
2020-06-28 23:59:28.381759: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with "Not found: Could not locate the credentials file.". Retrieving token from GCE failed with "Not found: Error executing an HTTP request: HTTP response code 404 with body '{"error":"invalid_request","error_description":"Service account not enabled on this instance"}'".
I0628 23:59:28.519958 139695953327872 dataset_info.py:427] Load pre-computed DatasetInfo (eg: splits, num examples,...) from GCS: oxford_flowers102/2.1.1
I0628 23:59:28.950128 139695953327872 dataset_info.py:358] Load dataset info from /tmp/tmpvdg6mnmotfds
I0628 23:59:28.966336 139695953327872 download_and_prepare.py:139] download_and_prepare for dataset oxford_flowers102/2.1.1...
I0628 23:59:28.967604 139695953327872 dataset_builder.py:345] Generating dataset oxford_flowers102 (/root/tensorflow_datasets/oxford_flowers102/2.1.1)
Downloading and preparing dataset oxford_flowers102/2.1.1 (download: 328.90 MiB, generated: 331.34 MiB, total: 660.25 MiB) to /root/tensorflow_datasets/oxford_flowers102/2.1.1...
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Extraction completed...: 0 file [00:00, ? file/s]I0628 23:59:29.135366 139695953327872 download_manager.py:477] Downloading https://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz into /root/tensorflow_datasets/downloads/robots.ox.ac.uk_vgg_flowers_102_102flowersoWedSp98maBn1wypsDib6T-q2NVbO40fwvTflmPmQpY.tgz.tmp.907358cf3cb346179068a023171b84c8...
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Dl Completed...:  67%|█████████████████▎        | 2/3 [00:03<00:00,  2.80 url/s]
Dl Size...:  16%|████▌                       | 54/328 [00:03<00:13, 20.89 MiB/s]

Extraction completed...: 0 file [00:03, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:03<00:00,  2.80 url/s]
Dl Size...:  17%|████▋                       | 55/328 [00:03<00:13, 19.62 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:03<00:00,  2.80 url/s]
Dl Size...:  17%|████▊                       | 56/328 [00:03<00:13, 19.62 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:03<00:00,  2.80 url/s]
Dl Size...:  17%|████▊                       | 57/328 [00:03<00:13, 19.62 MiB/s]

Extraction completed...: 0 file [00:03, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  18%|████▉                       | 58/328 [00:04<00:12, 20.90 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  18%|█████                       | 59/328 [00:04<00:12, 20.90 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  18%|█████                       | 60/328 [00:04<00:12, 20.90 MiB/s]

Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  19%|█████▏                      | 61/328 [00:04<00:12, 21.97 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  19%|█████▎                      | 62/328 [00:04<00:12, 21.97 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  19%|█████▍                      | 63/328 [00:04<00:12, 21.97 MiB/s]

Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  20%|█████▍                      | 64/328 [00:04<00:13, 20.27 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  20%|█████▌                      | 65/328 [00:04<00:12, 20.27 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  20%|█████▋                      | 66/328 [00:04<00:12, 20.27 MiB/s]

Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  20%|█████▋                      | 67/328 [00:04<00:12, 21.23 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  21%|█████▊                      | 68/328 [00:04<00:12, 21.23 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  21%|█████▉                      | 69/328 [00:04<00:12, 21.23 MiB/s]

Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  21%|█████▉                      | 70/328 [00:04<00:11, 22.39 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  22%|██████                      | 71/328 [00:04<00:11, 22.39 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  22%|██████▏                     | 72/328 [00:04<00:11, 22.39 MiB/s]

Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  22%|██████▏                     | 73/328 [00:04<00:12, 20.43 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  23%|██████▎                     | 74/328 [00:04<00:12, 20.43 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  23%|██████▍                     | 75/328 [00:04<00:12, 20.43 MiB/s]

Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  23%|██████▍                     | 76/328 [00:04<00:11, 21.59 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  23%|██████▌                     | 77/328 [00:04<00:11, 21.59 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  24%|██████▋                     | 78/328 [00:04<00:11, 21.59 MiB/s]

Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:04<00:00,  2.80 url/s]
Dl Size...:  24%|██████▋                     | 79/328 [00:04<00:11, 22.56 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  24%|██████▊                     | 80/328 [00:05<00:10, 22.56 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  25%|██████▉                     | 81/328 [00:05<00:10, 22.56 MiB/s]

Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  25%|███████                     | 82/328 [00:05<00:11, 22.22 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  25%|███████                     | 83/328 [00:05<00:11, 22.22 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  26%|███████▏                    | 84/328 [00:05<00:10, 22.22 MiB/s]

Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  26%|███████▎                    | 85/328 [00:05<00:10, 22.42 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  26%|███████▎                    | 86/328 [00:05<00:10, 22.42 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  27%|███████▍                    | 87/328 [00:05<00:10, 22.42 MiB/s]

Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  27%|███████▌                    | 88/328 [00:05<00:10, 23.33 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  27%|███████▌                    | 89/328 [00:05<00:10, 23.33 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  27%|███████▋                    | 90/328 [00:05<00:10, 23.33 MiB/s]

Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  28%|███████▊                    | 91/328 [00:05<00:09, 24.27 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  28%|███████▊                    | 92/328 [00:05<00:09, 24.27 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  28%|███████▉                    | 93/328 [00:05<00:09, 24.27 MiB/s]

Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  29%|████████                    | 94/328 [00:05<00:09, 24.93 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  29%|████████                    | 95/328 [00:05<00:09, 24.93 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  29%|████████▏                   | 96/328 [00:05<00:09, 24.93 MiB/s]

Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  30%|████████▎                   | 97/328 [00:05<00:09, 25.49 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  30%|████████▎                   | 98/328 [00:05<00:09, 25.49 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  30%|████████▍                   | 99/328 [00:05<00:08, 25.49 MiB/s]

Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  30%|████████▏                  | 100/328 [00:05<00:09, 24.39 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  31%|████████▎                  | 101/328 [00:05<00:09, 24.39 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  31%|████████▍                  | 102/328 [00:05<00:09, 24.39 MiB/s]

Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  31%|████████▍                  | 103/328 [00:05<00:08, 25.02 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  32%|████████▌                  | 104/328 [00:05<00:08, 25.02 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:05<00:00,  2.80 url/s]
Dl Size...:  32%|████████▋                  | 105/328 [00:05<00:08, 25.02 MiB/s]

Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  32%|████████▋                  | 106/328 [00:06<00:09, 24.62 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  33%|████████▊                  | 107/328 [00:06<00:08, 24.62 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  33%|████████▉                  | 108/328 [00:06<00:08, 24.62 MiB/s]

Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  33%|████████▉                  | 109/328 [00:06<00:08, 25.36 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  34%|█████████                  | 110/328 [00:06<00:08, 25.36 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  34%|█████████▏                 | 111/328 [00:06<00:08, 25.36 MiB/s]

Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  34%|█████████▏                 | 112/328 [00:06<00:08, 25.57 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  34%|█████████▎                 | 113/328 [00:06<00:08, 25.57 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  35%|█████████▍                 | 114/328 [00:06<00:08, 25.57 MiB/s]

Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  35%|█████████▍                 | 115/328 [00:06<00:08, 26.32 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  35%|█████████▌                 | 116/328 [00:06<00:08, 26.32 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  36%|█████████▋                 | 117/328 [00:06<00:08, 26.32 MiB/s]

Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  36%|█████████▋                 | 118/328 [00:06<00:07, 26.61 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  36%|█████████▊                 | 119/328 [00:06<00:07, 26.61 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  37%|█████████▉                 | 120/328 [00:06<00:07, 26.61 MiB/s]

Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  37%|█████████▉                 | 121/328 [00:06<00:07, 26.55 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  37%|██████████                 | 122/328 [00:06<00:07, 26.55 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  38%|██████████▏                | 123/328 [00:06<00:07, 26.55 MiB/s]

Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  38%|██████████▏                | 124/328 [00:06<00:07, 26.84 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  38%|██████████▎                | 125/328 [00:06<00:07, 26.84 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  38%|██████████▎                | 126/328 [00:06<00:07, 26.84 MiB/s]

Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  39%|██████████▍                | 127/328 [00:06<00:07, 25.73 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  39%|██████████▌                | 128/328 [00:06<00:07, 25.73 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  39%|██████████▌                | 129/328 [00:06<00:07, 25.73 MiB/s]

Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:06<00:00,  2.80 url/s]
Dl Size...:  40%|██████████▋                | 130/328 [00:06<00:07, 26.23 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  40%|██████████▊                | 131/328 [00:07<00:07, 26.23 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  40%|██████████▊                | 132/328 [00:07<00:07, 26.23 MiB/s]

Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  41%|██████████▉                | 133/328 [00:07<00:07, 26.45 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  41%|███████████                | 134/328 [00:07<00:07, 26.45 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  41%|███████████                | 135/328 [00:07<00:07, 26.45 MiB/s]

Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  41%|███████████▏               | 136/328 [00:07<00:07, 25.28 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  42%|███████████▎               | 137/328 [00:07<00:07, 25.28 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  42%|███████████▎               | 138/328 [00:07<00:07, 25.28 MiB/s]

Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  42%|███████████▍               | 139/328 [00:07<00:07, 24.85 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  43%|███████████▌               | 140/328 [00:07<00:07, 24.85 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  43%|███████████▌               | 141/328 [00:07<00:07, 24.85 MiB/s]

Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  43%|███████████▋               | 142/328 [00:07<00:07, 25.35 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  44%|███████████▊               | 143/328 [00:07<00:07, 25.35 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  44%|███████████▊               | 144/328 [00:07<00:07, 25.35 MiB/s]

Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  44%|███████████▉               | 145/328 [00:07<00:07, 25.54 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  45%|████████████               | 146/328 [00:07<00:07, 25.54 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  45%|████████████               | 147/328 [00:07<00:07, 25.54 MiB/s]

Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  45%|████████████▏              | 148/328 [00:07<00:06, 26.10 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  45%|████████████▎              | 149/328 [00:07<00:06, 26.10 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  46%|████████████▎              | 150/328 [00:07<00:06, 26.10 MiB/s]

Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  46%|████████████▍              | 151/328 [00:07<00:06, 25.53 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  46%|████████████▌              | 152/328 [00:07<00:06, 25.53 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  47%|████████████▌              | 153/328 [00:07<00:06, 25.53 MiB/s]

Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  47%|████████████▋              | 154/328 [00:07<00:06, 25.82 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  47%|████████████▊              | 155/328 [00:07<00:06, 25.82 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:07<00:00,  2.80 url/s]
Dl Size...:  48%|████████████▊              | 156/328 [00:07<00:06, 25.82 MiB/s]

Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  48%|████████████▉              | 157/328 [00:08<00:06, 24.98 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  48%|█████████████              | 158/328 [00:08<00:06, 24.98 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  48%|█████████████              | 159/328 [00:08<00:06, 24.98 MiB/s]

Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  49%|█████████████▏             | 160/328 [00:08<00:06, 24.34 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  49%|█████████████▎             | 161/328 [00:08<00:06, 24.34 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  49%|█████████████▎             | 162/328 [00:08<00:06, 24.34 MiB/s]

Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  50%|█████████████▍             | 163/328 [00:08<00:06, 25.32 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  50%|█████████████▌             | 164/328 [00:08<00:06, 25.32 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  50%|█████████████▌             | 165/328 [00:08<00:06, 25.32 MiB/s]

Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  51%|█████████████▋             | 166/328 [00:08<00:06, 25.73 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  51%|█████████████▋             | 167/328 [00:08<00:06, 25.73 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  51%|█████████████▊             | 168/328 [00:08<00:06, 25.73 MiB/s]

Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  52%|█████████████▉             | 169/328 [00:08<00:06, 26.06 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  52%|█████████████▉             | 170/328 [00:08<00:06, 26.06 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  52%|██████████████             | 171/328 [00:08<00:06, 26.06 MiB/s]

Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  52%|██████████████▏            | 172/328 [00:08<00:06, 25.62 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  53%|██████████████▏            | 173/328 [00:08<00:06, 25.62 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  53%|██████████████▎            | 174/328 [00:08<00:06, 25.62 MiB/s]

Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  53%|██████████████▍            | 175/328 [00:08<00:05, 26.06 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  54%|██████████████▍            | 176/328 [00:08<00:05, 26.06 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  54%|██████████████▌            | 177/328 [00:08<00:05, 26.06 MiB/s]

Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  54%|██████████████▋            | 178/328 [00:08<00:05, 26.31 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  55%|██████████████▋            | 179/328 [00:08<00:05, 26.31 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  55%|██████████████▊            | 180/328 [00:08<00:05, 26.31 MiB/s]

Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  55%|██████████████▉            | 181/328 [00:08<00:05, 25.25 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:08<00:00,  2.80 url/s]
Dl Size...:  55%|██████████████▉            | 182/328 [00:08<00:05, 25.25 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  56%|███████████████            | 183/328 [00:09<00:05, 25.25 MiB/s]

Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  56%|███████████████▏           | 184/328 [00:09<00:05, 26.05 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  56%|███████████████▏           | 185/328 [00:09<00:05, 26.05 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  57%|███████████████▎           | 186/328 [00:09<00:05, 26.05 MiB/s]

Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  57%|███████████████▍           | 187/328 [00:09<00:05, 25.60 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  57%|███████████████▍           | 188/328 [00:09<00:05, 25.60 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  58%|███████████████▌           | 189/328 [00:09<00:05, 25.60 MiB/s]

Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  58%|███████████████▋           | 190/328 [00:09<00:05, 26.07 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  58%|███████████████▋           | 191/328 [00:09<00:05, 26.07 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  59%|███████████████▊           | 192/328 [00:09<00:05, 26.07 MiB/s]

Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  59%|███████████████▉           | 193/328 [00:09<00:05, 26.47 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  59%|███████████████▉           | 194/328 [00:09<00:05, 26.47 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  59%|████████████████           | 195/328 [00:09<00:05, 26.47 MiB/s]

Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  60%|████████████████▏          | 196/328 [00:09<00:05, 26.31 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  60%|████████████████▏          | 197/328 [00:09<00:04, 26.31 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  60%|████████████████▎          | 198/328 [00:09<00:04, 26.31 MiB/s]

Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  61%|████████████████▍          | 199/328 [00:09<00:05, 25.80 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  61%|████████████████▍          | 200/328 [00:09<00:04, 25.80 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  61%|████████████████▌          | 201/328 [00:09<00:04, 25.80 MiB/s]

Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  62%|████████████████▋          | 202/328 [00:09<00:04, 26.53 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  62%|████████████████▋          | 203/328 [00:09<00:04, 26.53 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  62%|████████████████▊          | 204/328 [00:09<00:04, 26.53 MiB/s]

Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  62%|████████████████▉          | 205/328 [00:09<00:04, 26.35 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  63%|████████████████▉          | 206/328 [00:09<00:04, 26.35 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  63%|█████████████████          | 207/328 [00:09<00:04, 26.35 MiB/s]

Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:09<00:00,  2.80 url/s]
Dl Size...:  63%|█████████████████          | 208/328 [00:09<00:04, 26.05 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  64%|█████████████████▏         | 209/328 [00:10<00:04, 26.05 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  64%|█████████████████▎         | 210/328 [00:10<00:04, 26.05 MiB/s]

Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  64%|█████████████████▎         | 211/328 [00:10<00:04, 26.72 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  65%|█████████████████▍         | 212/328 [00:10<00:04, 26.72 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  65%|█████████████████▌         | 213/328 [00:10<00:04, 26.72 MiB/s]

Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  65%|█████████████████▌         | 214/328 [00:10<00:04, 26.45 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  66%|█████████████████▋         | 215/328 [00:10<00:04, 26.45 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  66%|█████████████████▊         | 216/328 [00:10<00:04, 26.45 MiB/s]

Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  66%|█████████████████▊         | 217/328 [00:10<00:04, 26.69 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  66%|█████████████████▉         | 218/328 [00:10<00:04, 26.69 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  67%|██████████████████         | 219/328 [00:10<00:04, 26.69 MiB/s]

Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  67%|██████████████████         | 220/328 [00:10<00:04, 26.21 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  67%|██████████████████▏        | 221/328 [00:10<00:04, 26.21 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  68%|██████████████████▎        | 222/328 [00:10<00:04, 26.21 MiB/s]

Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  68%|██████████████████▎        | 223/328 [00:10<00:03, 26.33 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  68%|██████████████████▍        | 224/328 [00:10<00:03, 26.33 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  69%|██████████████████▌        | 225/328 [00:10<00:03, 26.33 MiB/s]

Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  69%|██████████████████▌        | 226/328 [00:10<00:03, 26.57 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  69%|██████████████████▋        | 227/328 [00:10<00:03, 26.57 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  70%|██████████████████▊        | 228/328 [00:10<00:03, 26.57 MiB/s]

Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  70%|██████████████████▊        | 229/328 [00:10<00:03, 26.84 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  70%|██████████████████▉        | 230/328 [00:10<00:03, 26.84 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  70%|███████████████████        | 231/328 [00:10<00:03, 26.84 MiB/s]

Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  71%|███████████████████        | 232/328 [00:10<00:03, 25.95 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  71%|███████████████████▏       | 233/328 [00:10<00:03, 25.95 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:10<00:00,  2.80 url/s]
Dl Size...:  71%|███████████████████▎       | 234/328 [00:10<00:03, 25.95 MiB/s]

Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  72%|███████████████████▎       | 235/328 [00:11<00:03, 26.67 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  72%|███████████████████▍       | 236/328 [00:11<00:03, 26.67 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  72%|███████████████████▌       | 237/328 [00:11<00:03, 26.67 MiB/s]

Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  73%|███████████████████▌       | 238/328 [00:11<00:03, 26.60 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  73%|███████████████████▋       | 239/328 [00:11<00:03, 26.60 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  73%|███████████████████▊       | 240/328 [00:11<00:03, 26.60 MiB/s]

Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  73%|███████████████████▊       | 241/328 [00:11<00:03, 27.04 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  74%|███████████████████▉       | 242/328 [00:11<00:03, 27.04 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  74%|████████████████████       | 243/328 [00:11<00:03, 27.04 MiB/s]

Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  74%|████████████████████       | 244/328 [00:11<00:03, 26.74 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  75%|████████████████████▏      | 245/328 [00:11<00:03, 26.74 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  75%|████████████████████▎      | 246/328 [00:11<00:03, 26.74 MiB/s]

Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  75%|████████████████████▎      | 247/328 [00:11<00:03, 26.30 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  76%|████████████████████▍      | 248/328 [00:11<00:03, 26.30 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  76%|████████████████████▍      | 249/328 [00:11<00:03, 26.30 MiB/s]

Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  76%|████████████████████▌      | 250/328 [00:11<00:02, 26.51 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  77%|████████████████████▋      | 251/328 [00:11<00:02, 26.51 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  77%|████████████████████▋      | 252/328 [00:11<00:02, 26.51 MiB/s]

Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  77%|████████████████████▊      | 253/328 [00:11<00:02, 26.40 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  77%|████████████████████▉      | 254/328 [00:11<00:02, 26.40 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  78%|████████████████████▉      | 255/328 [00:11<00:02, 26.40 MiB/s]

Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  78%|█████████████████████      | 256/328 [00:11<00:02, 26.67 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  78%|█████████████████████▏     | 257/328 [00:11<00:02, 26.67 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  79%|█████████████████████▏     | 258/328 [00:11<00:02, 26.67 MiB/s]

Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  79%|█████████████████████▎     | 259/328 [00:11<00:02, 26.88 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  79%|█████████████████████▍     | 260/328 [00:11<00:02, 26.88 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:11<00:00,  2.80 url/s]
Dl Size...:  80%|█████████████████████▍     | 261/328 [00:11<00:02, 26.88 MiB/s]

Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  80%|█████████████████████▌     | 262/328 [00:12<00:02, 26.86 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  80%|█████████████████████▋     | 263/328 [00:12<00:02, 26.86 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  80%|█████████████████████▋     | 264/328 [00:12<00:02, 26.86 MiB/s]

Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  81%|█████████████████████▊     | 265/328 [00:12<00:02, 26.89 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  81%|█████████████████████▉     | 266/328 [00:12<00:02, 26.89 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  81%|█████████████████████▉     | 267/328 [00:12<00:02, 26.89 MiB/s]

Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  82%|██████████████████████     | 268/328 [00:12<00:02, 26.00 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  82%|██████████████████████▏    | 269/328 [00:12<00:02, 26.00 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  82%|██████████████████████▏    | 270/328 [00:12<00:02, 26.00 MiB/s]

Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  83%|██████████████████████▎    | 271/328 [00:12<00:02, 26.47 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  83%|██████████████████████▍    | 272/328 [00:12<00:02, 26.47 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  83%|██████████████████████▍    | 273/328 [00:12<00:02, 26.47 MiB/s]

Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  84%|██████████████████████▌    | 274/328 [00:12<00:02, 26.21 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  84%|██████████████████████▋    | 275/328 [00:12<00:02, 26.21 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  84%|██████████████████████▋    | 276/328 [00:12<00:01, 26.21 MiB/s]

Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  84%|██████████████████████▊    | 277/328 [00:12<00:01, 26.61 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  85%|██████████████████████▉    | 278/328 [00:12<00:01, 26.61 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  85%|██████████████████████▉    | 279/328 [00:12<00:01, 26.61 MiB/s]

Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  85%|███████████████████████    | 280/328 [00:12<00:01, 25.71 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  86%|███████████████████████▏   | 281/328 [00:12<00:01, 25.71 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  86%|███████████████████████▏   | 282/328 [00:12<00:01, 25.71 MiB/s]

Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  86%|███████████████████████▎   | 283/328 [00:12<00:01, 26.23 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  87%|███████████████████████▍   | 284/328 [00:12<00:01, 26.23 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  87%|███████████████████████▍   | 285/328 [00:12<00:01, 26.23 MiB/s]

Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  87%|███████████████████████▌   | 286/328 [00:12<00:01, 26.61 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  88%|███████████████████████▋   | 287/328 [00:12<00:01, 26.61 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:12<00:00,  2.80 url/s]
Dl Size...:  88%|███████████████████████▋   | 288/328 [00:12<00:01, 26.61 MiB/s]

Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  88%|███████████████████████▊   | 289/328 [00:13<00:01, 26.82 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  88%|███████████████████████▊   | 290/328 [00:13<00:01, 26.82 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  89%|███████████████████████▉   | 291/328 [00:13<00:01, 26.82 MiB/s]

Extraction completed...: 0 file [00:13, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  89%|████████████████████████   | 292/328 [00:13<00:01, 26.50 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  89%|████████████████████████   | 293/328 [00:13<00:01, 26.50 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  90%|████████████████████████▏  | 294/328 [00:13<00:01, 26.50 MiB/s]

Extraction completed...: 0 file [00:13, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  90%|████████████████████████▎  | 295/328 [00:13<00:01, 26.47 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  90%|████████████████████████▎  | 296/328 [00:13<00:01, 26.47 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  91%|████████████████████████▍  | 297/328 [00:13<00:01, 26.47 MiB/s]

Extraction completed...: 0 file [00:13, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  91%|████████████████████████▌  | 298/328 [00:13<00:01, 27.06 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  91%|████████████████████████▌  | 299/328 [00:13<00:01, 27.06 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  91%|████████████████████████▋  | 300/328 [00:13<00:01, 27.06 MiB/s]

Extraction completed...: 0 file [00:13, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  92%|████████████████████████▊  | 301/328 [00:13<00:01, 26.48 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  92%|████████████████████████▊  | 302/328 [00:13<00:00, 26.48 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  92%|████████████████████████▉  | 303/328 [00:13<00:00, 26.48 MiB/s]

Extraction completed...: 0 file [00:13, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  93%|█████████████████████████  | 304/328 [00:13<00:00, 26.64 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  93%|█████████████████████████  | 305/328 [00:13<00:00, 26.64 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  93%|█████████████████████████▏ | 306/328 [00:13<00:00, 26.64 MiB/s]

Extraction completed...: 0 file [00:13, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  94%|█████████████████████████▎ | 307/328 [00:13<00:00, 26.32 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  94%|█████████████████████████▎ | 308/328 [00:13<00:00, 26.32 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  94%|█████████████████████████▍ | 309/328 [00:13<00:00, 26.32 MiB/s]

Extraction completed...: 0 file [00:13, ? file/s]
Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  95%|█████████████████████████▌ | 310/328 [00:13<00:00, 24.39 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  95%|█████████████████████████▌ | 311/328 [00:13<00:00, 24.39 MiB/s]

Dl Completed...:  67%|█████████████████▎        | 2/3 [00:13<00:00,  2.80 url/s]
Dl Size...:  95%|█████████████████████████▋ | 312/328 [00:13<00:00, 24.39 MiB/s]

Extraction completed...: 0 file [00:13, ? file/s]
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I0628 23:59:51.706899 139695953327872 dataset_builder.py:946] Generating split train
Shuffling and writing examples to /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteD2YF12/oxford_flowers102-train.tfrecord
 99%|████████████████████████████▌| 1006/1020 [00:00<00:00, 10059.34 examples/s]I0628 23:59:52.696656 139695953327872 tfrecords_writer.py:230] Done writing /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteD2YF12/oxford_flowers102-train.tfrecord. Shard lengths: [1020]
I0628 23:59:52.699883 139695953327872 dataset_builder.py:946] Generating split test
Shuffling and writing examples to /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteD2YF12/oxford_flowers102-test.tfrecord
 91%|██████████████████████████▎  | 5591/6149 [00:00<00:00, 11177.72 examples/s]I0628 23:59:58.008367 139695953327872 tfrecords_writer.py:230] Done writing /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteD2YF12/oxford_flowers102-test.tfrecord. Shard lengths: [3074, 3075]
I0628 23:59:58.015285 139695953327872 dataset_builder.py:946] Generating split validation
Shuffling and writing examples to /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteD2YF12/oxford_flowers102-validation.tfrecord
  0%|                                           | 0/1020 [00:00<?, ? examples/s]I0628 23:59:58.858095 139695953327872 tfrecords_writer.py:230] Done writing /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteD2YF12/oxford_flowers102-validation.tfrecord. Shard lengths: [1020]
I0628 23:59:58.862737 139695953327872 dataset_builder.py:400] Skipping computing stats for mode ComputeStatsMode.SKIP.
Dataset oxford_flowers102 downloaded and prepared to /root/tensorflow_datasets/oxford_flowers102/2.1.1. Subsequent calls will reuse this data.
name: "oxford_flowers102"
description: "The Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly occurring\nin the United Kingdom. Each class consists of between 40 and 258 images. The images have\nlarge scale, pose and light variations. In addition, there are categories that have large\nvariations within the category and several very similar categories.\n\nThe dataset is divided into a training set, a validation set and a test set.\nThe training set and validation set each consist of 10 images per class (totalling 1020 images each).\nThe test set consists of the remaining 6149 images (minimum 20 per class)."
citation: "@InProceedings{Nilsback08,\n   author = \"Nilsback, M-E. and Zisserman, A.\",\n   title = \"Automated Flower Classification over a Large Number of Classes\",\n   booktitle = \"Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing\",\n   year = \"2008\",\n   month = \"Dec\"\n}"
location {
  urls: "https://www.robots.ox.ac.uk/~vgg/data/flowers/102/"
}
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splits {
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version: "2.1.1"
download_size: 344878000

In [4]:
# Load the dataset with TensorFlow Datasets. Hint: use tfds.load()
oxford_data, dataset_info = tfds.load('oxford_flowers102', shuffle_files=True, with_info = True)
In [5]:
# Create a training set, a validation set and a test set.
training_data = oxford_data['train']
test_data = oxford_data['test']
validation_data = oxford_data['validation']

Explore the Dataset

In [6]:
# Get the number of examples in each set from the dataset info.
num_training_examples = dataset_info.splits['train'].num_examples
num_test_examples = dataset_info.splits['test'].num_examples
num_validation_examples = dataset_info.splits['validation'].num_examples

# Get the number of classes in the dataset from the dataset info.
total_number_of_classes = dataset_info.features['label'].num_classes
In [7]:
print("Total # of Training sampled: ", num_training_examples)

print("Total # of Validations sampled: ", num_validation_examples)

print("Total # of Tests sampled: ", num_test_examples)
print("\n------------------")
print("Total # of Classes: ", total_number_of_classes)
Total # of Training sampled:  1020
Total # of Validations sampled:  1020
Total # of Tests sampled:  6149

------------------
Total # of Classes:  102
In [8]:
# Print the shape and corresponding label of 3 images in the training set.
i = 0
for image in training_data.take(3):
    i += 1
    print(f"Image # {i}: ", image['image'].shape, image['label'])
    
Image # 1:  (500, 505, 3) tf.Tensor(51, shape=(), dtype=int64)
Image # 2:  (500, 667, 3) tf.Tensor(72, shape=(), dtype=int64)
Image # 3:  (500, 666, 3) tf.Tensor(84, shape=(), dtype=int64)
In [9]:
# Plot 1 image from the training set. 

for image in training_data.take(1):
    imageArray = image['image'].numpy().squeeze()
    label = image['label'].numpy()

plt.imshow(imageArray, cmap = plt.cm.binary)
plt.colorbar()
plt.title(label)
plt.show()

print('Image Label: ', label)
Image Label:  72

Label Mapping

You'll also need to load in a mapping from label to category name. You can find this in the file label_map.json. It's a JSON object which you can read in with the json module. This will give you a dictionary mapping the integer coded labels to the actual names of the flowers.

In [10]:
import json
with open('label_map.json', 'r') as f:
    class_names = json.load(f)
class_names_final = dict()

# Class Alignment b/t data and image classifications.
for key in class_names:
    class_names_final[str(int(key)-1)] = class_names[key]
In [11]:
# Plot 1 image from the training set. Set the title 
# of the plot to the corresponding class name. 
import matplotlib.pyplot as plt

plt.imshow(imageArray, cmap = plt.cm.binary)
plt.colorbar()
plt.title(class_names_final[str(label)])
plt.show()

Create Pipeline

In [12]:
training_data
Out[12]:
<_OptionsDataset shapes: {file_name: (), image: (None, None, 3), label: ()}, types: {file_name: tf.string, image: tf.uint8, label: tf.int64}>
In [13]:
# Create a pipeline for each set.
# image['image'].shape,image['label']
def normalize(image):
    imageObj = tf.image.resize(image['image'],(224,224))

    imageObj /= 255
    label = image['label']
    label = tf.one_hot(label,total_number_of_classes)

    return(imageObj, label)

batch_size = 32

total_num_examples = dataset_info.splits['train'].num_examples

# num_training_examples = (total_num_examples) // 100


# Finalized batches for DL model. 
training_batches = training_data.shuffle(num_training_examples//4).map(normalize).batch(batch_size).prefetch(1)

validation_batches = validation_data.shuffle(num_training_examples//4).map(normalize).batch(batch_size).prefetch(1)
testing_batches = test_data.shuffle(num_training_examples//4).map(normalize).batch(batch_size).prefetch(1)

Build and Train the Classifier

Now that the data is ready, it's time to build and train the classifier. You should use the MobileNet pre-trained model from TensorFlow Hub to get the image features. Build and train a new feed-forward classifier using those features.

We're going to leave this part up to you. If you want to talk through it with someone, chat with your fellow students!

Refer to the rubric for guidance on successfully completing this section. Things you'll need to do:

  • Load the MobileNet pre-trained network from TensorFlow Hub.
  • Define a new, untrained feed-forward network as a classifier.
  • Train the classifier.
  • Plot the loss and accuracy values achieved during training for the training and validation set.
  • Save your trained model as a Keras model.

We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!

When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right.

Note for Workspace users: One important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module. Also, If your model is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with ls -lh), you should reduce the size of your hidden layers and train again.

In [14]:
# Build and train your network.

URL = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
feature_extractor = hub.KerasLayer(URL, input_shape=(224, 224,3))
feature_extractor.trainable = False
In [15]:
model = tf.keras.Sequential()
In [16]:
model.add(feature_extractor)
model.add(tf.keras.layers.Dense(600,activation='relu'))
model.add(tf.keras.layers.Dense(300,activation='relu'))
model.add(tf.keras.layers.Dense(total_number_of_classes, activation = 'softmax'))
In [17]:
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
keras_layer (KerasLayer)     (None, 1280)              2257984   
_________________________________________________________________
dense (Dense)                (None, 600)               768600    
_________________________________________________________________
dense_1 (Dense)              (None, 300)               180300    
_________________________________________________________________
dense_2 (Dense)              (None, 102)               30702     
=================================================================
Total params: 3,237,586
Trainable params: 979,602
Non-trainable params: 2,257,984
_________________________________________________________________
In [18]:
model.compile(optimizer = 'adam',
             loss = 'categorical_crossentropy',
             metrics = ['accuracy'])
In [19]:
EPOCHS = 12

history = model.fit(training_batches,
                    epochs=EPOCHS,
                    validation_data=validation_batches)
Epoch 1/12
32/32 [==============================] - 22s 690ms/step - loss: 4.1411 - accuracy: 0.1255 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00
Epoch 2/12
32/32 [==============================] - 6s 194ms/step - loss: 1.7855 - accuracy: 0.5961 - val_loss: 1.5773 - val_accuracy: 0.6127
Epoch 3/12
32/32 [==============================] - 6s 193ms/step - loss: 0.6669 - accuracy: 0.8510 - val_loss: 1.3396 - val_accuracy: 0.6422
Epoch 4/12
32/32 [==============================] - 6s 191ms/step - loss: 0.2893 - accuracy: 0.9480 - val_loss: 1.1261 - val_accuracy: 0.7059
Epoch 5/12
32/32 [==============================] - 6s 195ms/step - loss: 0.1574 - accuracy: 0.9716 - val_loss: 1.0195 - val_accuracy: 0.7235
Epoch 6/12
32/32 [==============================] - 6s 193ms/step - loss: 0.0622 - accuracy: 0.9971 - val_loss: 0.8982 - val_accuracy: 0.7637
Epoch 7/12
32/32 [==============================] - 6s 192ms/step - loss: 0.0310 - accuracy: 0.9990 - val_loss: 0.8240 - val_accuracy: 0.7882
Epoch 8/12
32/32 [==============================] - 6s 190ms/step - loss: 0.0188 - accuracy: 0.9990 - val_loss: 0.7967 - val_accuracy: 0.7873
Epoch 9/12
32/32 [==============================] - 6s 191ms/step - loss: 0.0103 - accuracy: 1.0000 - val_loss: 0.7759 - val_accuracy: 0.7971
Epoch 10/12
32/32 [==============================] - 6s 188ms/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.7702 - val_accuracy: 0.7971
Epoch 11/12
32/32 [==============================] - 6s 189ms/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 0.7707 - val_accuracy: 0.7980
Epoch 12/12
32/32 [==============================] - 6s 188ms/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.7671 - val_accuracy: 0.7951
In [20]:
# Acquire Metrics for visualization.

epochs_range = range(EPOCHS)

training_accuracy = history.history['accuracy']
training_loss = history.history['loss']
val_accuracy = history.history['val_accuracy']
val_loss = history.history['val_loss']


# Plot the loss and accuracy values achieved during training 
# for the training and validation set.
# print("History Keys: ", history.history.keys())

a = plt.figure(figsize=(12, 8))
a.suptitle('Model Loss & Accuracy Plots', fontsize= 12)

plt.subplot(1,2,1)
plt.xlabel('Epochs')
plt.plot(epochs_range,training_loss, label = 'Training: Loss')
plt.plot(epochs_range, val_loss, label = 'Validation: Loss')

plt.legend()


plt.subplot(1,2,2)
plt.xlabel('Epochs')
plt.plot(epochs_range,training_accuracy, label = 'Training: Accuracy')
plt.plot(epochs_range, val_accuracy, label = 'Validation: Accuracy')
plt.legend()
plt.show()

Testing your Network

It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. You should be able to reach around 70% accuracy on the test set if the model has been trained well.

In [21]:
# Print the loss and accuracy values achieved on the entire test set.
modelResults = model.predict(testing_batches)
modelResults_loss, modelResults_accuracy = model.evaluate(testing_batches)
193/193 [==============================] - 16s 83ms/step - loss: 0.9479 - accuracy: 0.7624
In [22]:
print(f"Model Results - Loss: {round(modelResults_loss, 2)}")
print(f"Model Results - Accuracy: {round(modelResults_accuracy, 2)}")
Model Results - Loss: 0.95
Model Results - Accuracy: 0.7599999904632568

Save the Model

Now that your network is trained, save the model so you can load it later for making inference. In the cell below save your model as a Keras model (i.e. save it as an HDF5 file).

In [23]:
# Save your trained model as a Keras model.
model.save('flower_model.h5')

Load the Keras Model

Load the Keras model you saved above.

In [24]:
# Load the Keras model

reloaded_model = tf.keras.models.load_model('flower_model.h5',
                                            custom_objects={'KerasLayer':hub.KerasLayer})

Inference for Classification

Now you'll write a function that uses your trained network for inference. Write a function called predict that takes an image, a model, and then returns the top $K$ most likely class labels along with the probabilities. The function call should look like:

probs, classes = predict(image_path, model, top_k)

If top_k=5 the output of the predict function should be something like this:

probs, classes = predict(image_path, model, 5)
print(probs)
print(classes)
> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]
> ['70', '3', '45', '62', '55']

Your predict function should use PIL to load the image from the given image_path. You can use the Image.open function to load the images. The Image.open() function returns an Image object. You can convert this Image object to a NumPy array by using the np.asarray() function.

The predict function will also need to handle pre-processing the input image such that it can be used by your model. We recommend you write a separate function called process_image that performs the pre-processing. You can then call the process_image function from the predict function.

Image Pre-processing

The process_image function should take in an image (in the form of a NumPy array) and return an image in the form of a NumPy array with shape (224, 224, 3).

First, you should convert your image into a TensorFlow Tensor and then resize it to the appropriate size using tf.image.resize.

Second, the pixel values of the input images are typically encoded as integers in the range 0-255, but the model expects the pixel values to be floats in the range 0-1. Therefore, you'll also need to normalize the pixel values.

Finally, convert your image back to a NumPy array using the .numpy() method.

In [25]:
# Create the process_image function
def process_image(image):
    imageObj = tf.image.resize(image,(224,224))
    imageObj_converted = imageObj.numpy()

    imageObj_converted /= 255

    return(imageObj_converted)

To check your process_image function we have provided 4 images in the ./test_images/ folder:

  • cautleya_spicata.jpg
  • hard-leaved_pocket_orchid.jpg
  • orange_dahlia.jpg
  • wild_pansy.jpg

The code below loads one of the above images using PIL and plots the original image alongside the image produced by your process_image function. If your process_image function works, the plotted image should be the correct size.

In [26]:
image_path = './test_images/hard-leaved_pocket_orchid.jpg'
im = Image.open(image_path)
test_image = np.asarray(im)

processed_test_image = process_image(test_image)

fig, (ax1, ax2) = plt.subplots(figsize=(10,10), ncols=2)
ax1.imshow(test_image)
ax1.set_title('Original Image')
ax2.imshow(processed_test_image)
ax2.set_title('Processed Image')
plt.tight_layout()
plt.show()

Once you can get images in the correct format, it's time to write the predict function for making inference with your model.

Inference

Remember, the predict function should take an image, a model, and then returns the top $K$ most likely class labels along with the probabilities. The function call should look like:

probs, classes = predict(image_path, model, top_k)

If top_k=5 the output of the predict function should be something like this:

probs, classes = predict(image_path, model, 5)
print(probs)
print(classes)
> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]
> ['70', '3', '45', '62', '55']

Your predict function should use PIL to load the image from the given image_path. You can use the Image.open function to load the images. The Image.open() function returns an Image object. You can convert this Image object to a NumPy array by using the np.asarray() function.

Note: The image returned by the process_image function is a NumPy array with shape (224, 224, 3) but the model expects the input images to be of shape (1, 224, 224, 3). This extra dimension represents the batch size. We suggest you use the np.expand_dims() function to add the extra dimension.

In [27]:
# Create the predict function
def predict(image_path, model, top_k):
    from PIL import Image
    
    im = Image.open(image_path)
    imageArray = np.asarray(im)
    imageArray_transformed = process_image(imageArray)
    
    pred_imageArray = model.predict(np.expand_dims(imageArray_transformed, axis=0))
    
    pred_imageArray_probs = pred_imageArray[0].tolist()
    
    values, indices= tf.math.top_k(pred_imageArray_probs, k=top_k)

    probs=values.numpy().tolist()
    classes=indices.numpy().tolist()
    


    return(probs, classes)
In [28]:
modelProdResults, modelClassResults = predict('./test_images/cautleya_spicata.jpg', reloaded_model, 5)

print("Top 5 probability results for classifying image: ", modelProdResults)
print("\n")
print("Top 5 classification results for classifying image, respectively: ", 
    [class_names_final[str(val)] for val in modelClassResults])
Top 5 probability results for classifying image:  [0.9920637011528015, 0.0029614116065204144, 0.002399832708761096, 0.0006375561351887882, 0.0006019893335178494]


Top 5 classification results for classifying image, respectively:  ['pink-yellow dahlia', 'bolero deep blue', 'globe thistle', 'great masterwort', 'fritillary']

Sanity Check

It's always good to check the predictions made by your model to make sure they are correct. To check your predictions we have provided 4 images in the ./test_images/ folder:

  • cautleya_spicata.jpg
  • hard-leaved_pocket_orchid.jpg
  • orange_dahlia.jpg
  • wild_pansy.jpg

In the cell below use matplotlib to plot the input image alongside the probabilities for the top 5 classes predicted by your model. Plot the probabilities as a bar graph. The plot should look like this:

You can convert from the class integer labels to actual flower names using class_names.

In [29]:
# Plot the input image along with the top 5 classes
def plot_prediction_images(image_path, model, k_top):
    from PIL import Image

    imageOpened = Image.open(image_path)
    test_image = np.asarray(imageOpened)

    resultsArray, classArray = predict(image_path, model, 5)

    
    processed_test_image = process_image(test_image)
    a = plt.figure(figsize=(12, 8))
    a.suptitle('Image Prediction Results', fontsize= 12)

    plt.subplot(1,2,1)
    plt.imshow(test_image)
    plt.title(f'Image: {image_path}')

    
    plt.subplot(1,2,2)
    plt.barh([class_names_final[str(i)] for i in classArray ], resultsArray)
    plt.xticks(rotation=90)
    plt.title(f'Classification Image: Probabilities - Top {k_top}')
    plt.show()

plot_prediction_images('./test_images/cautleya_spicata.jpg', reloaded_model, 6)
plot_prediction_images('./test_images/wild_pansy.jpg', reloaded_model, 6)
plot_prediction_images('./test_images/orange_dahlia.jpg', reloaded_model, 5)
plot_prediction_images('./test_images/hard-leaved_pocket_orchid.jpg', reloaded_model, 4)