Image classification

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This tutorial shows how to classify images of flowers using a tf.keras.Sequential model and load data using tf.keras.utils.image_dataset_from_directory. It demonstrates the following concepts:

  • Efficiently loading a dataset off disk.
  • Identifying overfitting and applying techniques to mitigate it, including data augmentation and dropout.

This tutorial follows a basic machine learning workflow:

  1. Examine and understand data
  2. Build an input pipeline
  3. Build the model
  4. Train the model
  5. Test the model
  6. Improve the model and repeat the process

In addition, the notebook demonstrates how to convert a saved model to a TensorFlow Lite model for on-device machine learning on mobile, embedded, and IoT devices.

Setup

Import TensorFlow and other necessary libraries:

import matplotlib.pyplot as plt
import numpy as np
import PIL
import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

Download and explore the dataset

This tutorial uses a dataset of about 3,700 photos of flowers. The dataset contains five sub-directories, one per class:

flower_photo/
  daisy/
  dandelion/
  roses/
  sunflowers/
  tulips/

import pathlib

dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos.tar'