Keras documentation: Data loading (original) (raw)
Keras data loading utilities, located in keras.utils, help you go from raw data on disk to a tf.data.Dataset object that can be used to efficiently train a model.
These loading utilites can be combined withpreprocessing layers to futher transform your input dataset before training.
Here's a quick example: let's say you have 10 folders, each containing 10,000 images from a different category, and you want to train a classifier that maps an image to its category.
Your training data folder would look like this:
training_data/ ...class_a/ ......a_image_1.jpg ......a_image_2.jpg ...class_b/ ......b_image_1.jpg ......b_image_2.jpg etc.
You may also have a validation data folder validation_data/ structured in the same way.
You could simply do:
`import keras
train_ds = keras.utils.image_dataset_from_directory( directory='training_data/', labels='inferred', label_mode='categorical', batch_size=32, image_size=(256, 256)) validation_ds = keras.utils.image_dataset_from_directory( directory='validation_data/', labels='inferred', label_mode='categorical', batch_size=32, image_size=(256, 256))
model = keras.applications.Xception( weights=None, input_shape=(256, 256, 3), classes=10) model.compile(optimizer='rmsprop', loss='categorical_crossentropy') model.fit(train_ds, epochs=10, validation_data=validation_ds) `
Available dataset loading utilities
Image data loading
- image_dataset_from_directory function
- load_img function
- img_to_array function
- save_img function
- array_to_img function