To help you get started, we've collected the most common ways that keras is being used within popular public projects.

brjathu / deepcaps / train.py View on Github

tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs', batch_size=args.batch_size, histogram_freq=int(args.debug), write_grads=False)
    checkpoint1 = CustomModelCheckpoint(model, args.save_dir + '/best_weights_1' + appendix + '.h5', monitor='val_capsnet_acc', 
                                        save_best_only=False, save_weights_only=True, verbose=1)

    checkpoint2 = CustomModelCheckpoint(model, args.save_dir + '/best_weights_2' + appendix + '.h5', monitor='val_capsnet_acc',
                                        save_best_only=True, save_weights_only=True, verbose=1)

    lr_decay = callbacks.LearningRateScheduler(schedule=lambda epoch: args.lr * 0.5**(epoch // 10))

    if(args.numGPU > 1):
        parallel_model = multi_gpu_model(model, gpus=args.numGPU)
    else:
        parallel_model = model

    if(not hard_training):
        parallel_model.compile(optimizer=optimizers.Adam(lr=args.lr), loss=[margin_loss, 'mse'], loss_weights=[1, 0.4], metrics={'capsnet': "accuracy"})
    else:
        parallel_model.compile(optimizer=optimizers.Adam(lr=args.lr), loss=[margin_loss_hard, 'mse'], loss_weights=[1, 0.4], metrics={'capsnet': "accuracy"})

    # Begin: Training with data augmentation
    def train_generator(x, y, batch_size, shift_fraction=args.shift_fraction):
        train_datagen = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False,
                                           samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0.1,
                                           width_shift_range=0.1, height_shift_range=0.1, shear_range=0.0,
                                           zoom_range=0.1, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=True,
                                           vertical_flip=False, rescale=None, preprocessing_function=None,
                                           data_format=None)  # shift up to 2 pixel for MNIST
        train_datagen.fit(x)
        generator = train_datagen.flow(x, y, batch_size=batch_size, shuffle=True)
        while True:
            x_batch, y_batch = generator.next()
            yield ([x_batch, y_batch], [y_batch, x_batch])

View more ways to use keras

Keras 3: Deep Learning for Humans

Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, and PyTorch. Effortlessly build and train models for computer vision, natural language processing, audio processing, timeseries forecasting, recommender systems, etc.

• Accelerated model development: Ship deep learning solutions faster thanks to the high-level UX of Keras and the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.
• State-of-the-art performance: By picking the backend that is the fastest for your model architecture (often JAX!), leverage speedups ranging from 20% to 350% compared to other frameworks. Benchmark here.
• Datacenter-scale training: Scale confidently from your laptop to large clusters of GPUs or TPUs.

Join nearly three million developers, from burgeoning startups to global enterprises, in harnessing the power of Keras 3.

Installation

Install with pip

Keras 3 is available on PyPI as keras. Note that Keras 2 remains available as the tf-keras package.

1. Install keras:

pip install keras --upgrade

2. Install backend package(s).

To use keras, you should also install the backend of choice: tensorflow, jax, or torch. Note that tensorflow is required for using certain Keras 3 features: certain preprocessing layers as well as tf.data pipelines.

Local installation

Minimal installation

Keras 3 is compatible with Linux and MacOS systems. For Windows users, we recommend using WSL2 to run Keras. To install a local development version:

1. Install dependencies:

pip install -r requirements.txt

2. Run installation command from the root directory.

python pip_build.py --install

3. Run API generation script when creating PRs that update keras_export public APIs:

./shell/api_gen.sh

Adding GPU support

The requirements.txt file will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we also provide a separate requirements-{backend}-cuda.txt for TensorFlow, JAX, and PyTorch. These install all CUDA dependencies via pip and expect a NVIDIA driver to be pre-installed. We recommend a clean python environment for each backend to avoid CUDA version mismatches. As an example, here is how to create a Jax GPU environment with conda:

conda create -y -n keras-jax python=3.10
conda activate keras-jax
pip install -r requirements-jax-cuda.txt
python pip_build.py --install

Configuring your backend

You can export the environment variable KERAS_BACKEND or you can edit your local config file at ~/.keras/keras.json to configure your backend. Available backend options are: "tensorflow", "jax", "torch". Example:

export KERAS_BACKEND="jax"

In Colab, you can do:

import os
os.environ["KERAS_BACKEND"] = "jax"

import keras

Note: The backend must be configured before importing keras, and the backend cannot be changed after the package has been imported.

Backwards compatibility

Keras 3 is intended to work as a drop-in replacement for tf.keras (when using the TensorFlow backend). Just take your existing tf.keras code, make sure that your calls to model.save() are using the up-to-date .keras format, and you're done.

If your tf.keras model does not include custom components, you can start running it on top of JAX or PyTorch immediately.

If it does include custom components (e.g. custom layers or a custom train_step()), it is usually possible to convert it to a backend-agnostic implementation in just a few minutes.

In addition, Keras models can consume datasets in any format, regardless of the backend you're using: you can train your models with your existing tf.data.Dataset pipelines or PyTorch DataLoaders.

Why use Keras 3?

• Run your high-level Keras workflows on top of any framework -- benefiting at will from the advantages of each framework, e.g. the scalability and performance of JAX or the production ecosystem options of TensorFlow.
• Write custom components (e.g. layers, models, metrics) that you can use in low-level workflows in any framework.
  • You can take a Keras model and train it in a training loop written from scratch in native TF, JAX, or PyTorch.
  • You can take a Keras model and use it as part of a PyTorch-native Module or as part of a JAX-native model function.
• Make your ML code future-proof by avoiding framework lock-in.
• As a PyTorch user: get access to power and usability of Keras, at last!
• As a JAX user: get access to a fully-featured, battle-tested, well-documented modeling and training library.

Read more in the Keras 3 release announcement.