Framework to ease training of generative models based on TensorFlow
Framework to ease training of generative models
SimpleGAN is a framework based on TensorFlow to make training of generative models easier. SimpleGAN provides high level APIs with customizability options to user which allows them to train a generative model with few lines of code.
Make sure you have the following packages installed
Latest stable release:
$ pip install simplegan
Latest Development release:
$ pip install git+https://github.com/grohith327/EasyGAN.git
>>> from simplegan.gan import DCGAN >>> gan = DCGAN() >>> train_ds = gan.load_data(use_mnist = True) >>> samples = gan.get_sample(train_ds, n_samples = 5) >>> gan.fit(train_ds = train_ds) >>> generated_samples = gan.generate_samples(n_samples = 5)
>>> from simplegan.autoencoder import ConvolutionalAutoencoder >>> autoenc = ConvolutionalAutoencoder() >>> train_ds, test_ds = autoenc.load_data(use_cifar10 = True) >>> train_sample = autoenc.get_sample(data = train_ds, n_samples = 5) >>> test_sample = autoenc.get_sample(data = test_ds, n_samples = 1) >>> autoenc.fit(train_ds = train_ds, epochs = 5, optimizer = 'RMSprop', learning_rate = 0.002) >>> generated_samples = autoenc.generate_samples(test_ds = test_ds.take(1))
To have a look at more examples in detail, check here
Check out the docs page
- Generative Adversarial Networks(GANs)
We appreciate all contributions. If you are planning to perform bug-fixes, add new features or models, please file an issue and discuss before making a pull request.
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