Introduction

This example shows how to do image classification from scratch（抓， 挠）, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset.

We use the image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation.

import tensorflow as tf from tensorflow

import keras from tensorflow.keras

import layers

Load the data: the Cats vs Dogs dataset
Raw data download
First, let's download the 786M ZIP archive of the raw data:

!curl -O https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip

!unzip -q kagglecatsanddogs_3367a.zip
!ls

Now we have a PetImages folder which contain two subfolders, Cat and Dog. Each subfolder contains image files for each category.

'
ls PetImages
'
Filter out corrupted images
When working with lots of real-world image data, corrupted images are a common occurence. Let's filter out badly-encoded images that do not feature the string "JFIF" in their header.
'''
import os

num_skipped = 0
for folder_name in ("Cat", "Dog"):
folder_path = os.path.join("PetImages", folder_name)
for fname in os.listdir(folder_path):
fpath = os.path.join(folder_path, fname)
try:
fobj = open(fpath, "rb")
is_jfif = tf.compat.as_bytes("JFIF") in fobj.peek(10)
finally:
fobj.close()

    if not is_jfif:
        num_skipped += 1
        # Delete corrupted image
        os.remove(fpath)

print("Deleted %d images" % num_skipped)
'''

Generate a Dataset

'''
image_size = (180, 180)
batch_size = 32

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
"PetImages",
validation_split=0.2,
subset="training",
seed=1337,
image_size=image_size,
batch_size=batch_size,
)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
"PetImages",
validation_split=0.2,
subset="validation",
seed=1337,
image_size=image_size,
batch_size=batch_size,
)
'''
Visualize the data
Here are the first 9 images in the training dataset. As you can see, label 1 is "dog" and label 0 is "cat".
'''
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(int(labels[i]))
plt.axis("off")

'''
Using image data augmentation
When you don't have a large image dataset, it's a good practice to artificially introduce sample diversity by applying random yet realistic transformations to the training images, such as random horizontal flipping or small random rotations. This helps expose the model to different aspects of the training data while slowing down overfitting.

'''

data_augmentation = keras.Sequential(
[
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.1),
]
)
'''
Let's visualize what the augmented samples look like, by applying data_augmentation repeatedly to the first image in the dataset:

'''
plt.figure(figsize=(10, 10))
for images, _ in train_ds.take(1):
for i in range(9):
augmented_images = data_augmentation(images)
ax = plt.subplot(3, 3, i + 1)
plt.imshow(augmented_images[0].numpy().astype("uint8"))
plt.axis("off")
'''
Standardizing the data
Our image are already in a standard size (180x180), as they are being yielded as contiguous float32 batches by our dataset. However, their RGB channel values are in the [0, 255] range. This is not ideal for a neural network; in general you should seek to make your input values small. Here, we will standardize values to be in the [0, 1] by using a Rescaling layer at the start of our model.

Two options to preprocess the data
There are two ways you could be using the data_augmentation preprocessor:

Option 1: Make it part of the model, like this:
'''
inputs = keras.Input(shape=input_shape)
x = data_augmentation(inputs)
x = layers.Rescaling(1./255)(x)
... # Rest of the model
'''
With this option, your data augmentation will happen on device, synchronously with the rest of the model execution, meaning that it will benefit from GPU acceleration.

Note that data augmentation is inactive at test time, so the input samples will only be augmented during fit(), not when calling evaluate() or predict().

If you're training on GPU, this is the better option.

Option 2: apply it to the dataset, so as to obtain a dataset that yields batches of augmented images, like this:
'''
augmented_train_ds = train_ds.map(
lambda x, y: (data_augmentation(x, training=True), y))
'''

With this option, your data augmentation will happen on CPU, asynchronously, and will be buffered before going into the model.

If you're training on CPU, this is the better option, since it makes data augmentation asynchronous and non-blocking.

In our case, we'll go with the first option.
Configure the dataset for performance
Let's make sure to use buffered prefetching so we can yield data from disk without having I/O becoming blocking:
'''
train_ds = train_ds.prefetch(buffer_size=32)
val_ds = val_ds.prefetch(buffer_size=32)
'''
Build a model
We'll build a small version of the Xception network. We haven't particularly tried to optimize the architecture; if you want to do a systematic search for the best model configuration, consider using KerasTuner.

Note that:

We start the model with the data_augmentation preprocessor, followed by a Rescaling layer.
We include a Dropout layer before the final classification layer.