Keras2 ImageDataGenerator or TensorFlow tf.data?

Question

With Keras2 being implemented into TensorFlow and TensorFlow 2.0 on the horizon, should you use Keras ImageDataGenerator with e.g, flow_from_directory or tf.data from TensorFlow which also can be used with fit_genearator of Keras now?

Will both methods will have their place by serving a different purpose or will tf.data be the new way to go and Keras generators deprecated in the future?

Thanks, I would like to take the path which keeps me up to date a bit longer in this fast moving field.

Asked By: Florida Man

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Source

Answer 1

Since its release, TensorFlow Dataset API is a default recommended way to construct input pipeline for any model build on TensorFlow backend, both Keras and low-level TensorFlow.
In later versions of TF 1.xx it can be directly used in tf.keras.Model.fit method as

model.fit(dataset, epochs)

It’s good both for rapid prototyping,

dataset = tf.data.Dataset.from_tensor_slices((train, test))
dataset = dataset.shuffle().repeat().batch()

And for building complex, high performance ETL pipelines
4. Upgrade your data input pipelines, more on this here https://www.tensorflow.org/guide/performance/datasets

As per official docs, in TF 2.0 it’ll also be the default way to input data to the model. https://www.tensorflow.org/alpha/guide/migration_guide

As by default, upcoming TensorFlow version will be executed eagerly, dataset object will become iterable and will be even easier to use.

Answered By: Sharky

Answer 2

For me, I prefer to build a generator with yield:

def generator(batch_size=4,path):
imgs=glob(path+'*.jpg')
while True:
    batch=[]
    for i in range(batch_size):
        idx=np.random.randint(0,len(imgs))
        img=cv.resize(cv.imread(imgs[idx]),(256,256))/255
        batch.append(img)
    batch=np.array(batch)
    yield batch

Then create the generator and input it to model.fit_generator, it will work.

You can choose data randomly like this or use some recurrent methods.

Though the code is rough, it is easy to change so that it can generate complex batch.

Note that this is a way to generate for TF 1.X with Keras2 and not with TensorFlow 2.0.

Answered By: hhz

Answer 3

Alongside custom defined Python generators, you can wrap the ImageDataGenerator from Keras inside tf.data.

The following snippets are taken from the TensorFlow 2.0 documentation.

img_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255, rotation_range=20)
ds = tf.data.Dataset.from_generator(
    img_gen.flow_from_directory, args=[flowers], 
    output_types=(tf.float32, tf.float32), 
    output_shapes = ([32,256,256,3],[32,5])
)

Therefore, one can still use the typical Keras ImageDataGenerator, you just need to wrap it into a tf.data.Dataset like above.

Answered By: Timbus Calin

Answer 4

Update 2022

On visiting the ImageDataGenerator documentation, there is now a deprecation message that says the following:

Deprecated: tf.keras.preprocessing.image.ImageDataGenerator is not recommended for new code. Prefer loading images with tf.keras.utils.image_dataset_from_directory and transforming the output tf.data.Dataset with preprocessing layers. For more information, see the tutorials for loading images and augmenting images, as well as the preprocessing layer guide.

Answered By: see

Keras2 ImageDataGenerator or TensorFlow tf.data?

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Update 2022